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comprehensive analytical chemistry
COMPREHENSIVE ANALYTICAL CHEMISTRY VOLUME 54 Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Linacre House, Jordan Hill, Oxford OX2 8DP, UK First edition 2008 Copyright r 2008 Elsevier B.V. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http:// www.elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing in Publication Data A catalog record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalogue record for this book is available from the Library of Congress ISBN: 978-0-444-53094-3 ISSN: 0166-526X For information on all Elsevier publications visit our website at books.elsevier.com Printed and bound in Hungary 08 09 10 11 12 10 9 8 7 6 5 4 3 2 1 ADVISORY BOARD Joseph A. Caruso University of Cincinnati, Cincinnati, OH, USA Hendrik Emons Joint Research Centre, Geel, Belgium Gary Hieftje Indiana University, Bloomington, IN, USA Kiyokatsu Jinno Toyohashi University of Technology, Toyohashi, Japan Uwe Karst University of Münster, Münster, Germany György Marko-Varga AstraZeneca, Lund, Sweden Janusz Pawliszyn University of Waterloo, Waterloo, Ont., Canada Susan Richardson US Environmental Protection Agency, Athens, GA, USA Wilson & Wilson’s COMPREHENSIVE ANALYTICAL CHEMISTRY Edited by D. BARCELÓ Research Professor Department of Environmental Chemistry IIQAB-CSIC Jordi Girona 18-26 08034 Barcelona Spain Wilson & Wilson’s COMPREHENSIVE ANALYTICAL CHEMISTRY ADVANCES IN FLOW INJECTION ANALYSIS AND RELATED TECHNIQUES VOLUME 54 Edited by SPAS D. KOLEV School of Chemistry, The University of Melbourne, Victoria 3010, Australia IAN D. MCKELVIE School of Chemistry, Monash University, Victoria 3800, Australia Amsterdam Boston Heidelberg London New York Oxford Paris San Diego San Francisco Singapore Sydney Tokyo CHAPT ER 18 Food, Beverages and Agricultural Applications Ildikó V. Tóth, Marcela A. Segundo and António O.S.S. Rangel Contents 1. Introduction 2. Applications: Beverages 3. Applications: Plants and Vegetables 4. Applications: Milk and Dairy Products 5. Applications: Meat and Fish Products 6. Miscellaneous Food Products Abbreviations References 513 514 545 546 547 548 548 549 1. INTRODUCTION Food quality and safety are major issues nowadays. Owing to increased concern on public health issues, national and international legislation has imposed stricter regulations on food control, both regarding chemical and microbiological aspects [1–3]. This scenario has produced a major impact on both agriculture and food industry practices. Companies and governmental certifying and regulatory agencies in this sector are faced with an increasing number of parameters to be monitored and the need to detect ever decreasing concentrations. Meeting these requirements demands novel analytical methods that are sensitive, efficient, and which provide significant improvements in laboratory productivity. This situation calls for the development of fast and automatic analytical methodologies for the food and beverage sector. Foodstuffs can be considered a complex matrix for a number of reasons: they are seldom homogeneous, and a solubilization process is normally required before analysis. This makes the sample pretreatment process relatively complex, and usually labour intensive. Additionally, these pretreatments might alter the Comprehensive Analytical Chemistry, Volume 54 ISSN: 0166-526X, DOI 10.1016/S0166-526X(08)00618-1 r 2008 Elsevier B.V. All rights reserved. 513 514 Ildikó V. Tóth et al. composition (namely the form of the analyte) of the foodstuff and impair the quality of the analysis itself. Moreover, food samples have a biological origin resulting in high temporal and spatial variability in analyte concentrations. Sample colour and turbidity may also make the analysis more difficult, due to the widespread use of spectrophotometric methods in this respect. In this context, flow analysis methods can be a powerful tool to overcome some of these difficulties and offer a relatively low-cost alternative. In fact, sample pretreatments such as sample digestion, using microwave, UV or ultrasound radiation-assisted processes can be efficiently carried out in flow systems, using an extremely limited amount of reagents and posing no danger to the operator. Mass separation processes (gas-diffusion, dialysis, ion-exchange) can also be performed in-manifold, allowing minimization of interferences and/ or analyte preconcentration. Regarding the instrumental measurement, flow methods make it possible to carry out all the necessary wet chemistry involved, including analyte derivatization and instrumental detection. Additionally, as kinetic time-based methods can be easily implemented, additional information can be obtained from the instrumental measurements. In this chapter, an overview of the flow methods described for the analysis of food, beverage and agricultural samples will be presented. The collection of publications was essentially obtained by using the search engine ISI Web of Knowledge. Owing to the large number of papers published so far on this subject (Figure 1), the decision was made to address only the advances reported since the year 2000. Information on previous works can be found in review papers published in the last decade focusing on different areas of food analysis [4–9], on particular flow techniques [10–13], on specific analytes [4,7,14–20] or on specific detection [21–24] and analyte-processing techniques [25–28]. As depicted in Figure 1a, the implementation of flow techniques in food analysis accompanies the trend observed for its application as analytical tool. Specific developments dealing with advances in sample pretreatment, such as digestion or mass separation methods, are not discussed in detail in this chapter, as these topics are the object of discussion in Chapters 6–9 of this book. Therefore, special emphasis will be given to the commodity involved. Considering the distribution of applications to specific classes of food (Figure 1b), the following categories were selected for review: beverages, milk and dairy products, meat and fish, fruits and vegetables and miscellaneous food products. The collection of publications from the year 2000 onwards is presented in Tables 1–6, where the main characteristics of the methodologies are summarized. The discussion that follows highlights some features of the flow systems, and some trends regarding the target analytes or groups of analyte. 2. APPLICATIONS: BEVERAGES A beverage is a drink specifically prepared for human consumption, other than water. Therefore, this designation includes alcoholic drinks (wine, beer, liquors, distilled spirits) and also coffee, fruit juices, tea and soft drinks, among others. Food, Beverages and Agricultural Applications 515 Figure 1 (a) Evolution of flow-injection application to food analysis, and (b) distribution by commodity. Owing to its liquid nature, this type of sample can be simply introduced into a flow system, without being weighed or solubilized. This aspect makes the automation of the whole analytical process easier, considering that any other pretreatment operation required can be included in the flow system before the determination of the target analyte. Furthermore, the possibility of direct sampling also allows direct, real-time monitoring of food processing, especially during must fermentation [29–34] or beer production [35]. 516 Table 1 Some of the analytical features of flow methods for alcoholic beverages Matrix Flow mode Detection system Working range Reference Alcohols Ethanol Ethanol Ethanol Beer, liquors, wine Must Wine FIA FIA FIA Amperometry Amperometry Amperometry [84] [31] [85] Wine Beer, spirits, wine, Wine Non-alcoholic beer Wine, spirits Sake, wine Beer, distilled liquors, white wines Beer, distilled liquors, wine Wine Wine Beer fermentation broth Wine Wine Wine Must Wine Wine Distilled spirits Wine Beer, wine Wine FIA FIA FIA FIA FIA FIA FIA Density measurement FTIR UV-Vis UV-Vis UV-Vis UV-Vis UV-Vis 0.020–2.0 mM NA 0.01 103– 0.75 103 M 0–40% (v/v) 0.05–15% (v/v) 1–20% (v/v) 0–100 mM 10–30% (v/v) 0.04–100 mM 5 106–1 103 M FIA UV-Vis 0.5–30% (v/v) [92] FIA SIA SIA UV-Vis Amperometry Amperometry 1.0–30.0% (v/v) 1–250 mM 0.15–30 mg L1 [93] [94] [35] SIA SIA MCFA FIA FIA FIA FIA SIA SIA MCFA UV-Vis UV-Vis Chemiluminescence Amperometry Amperometry Fluorimetry Potentiometry UV-Vis UV-Vis UV-Vis 0.008–0.024% (v/v) 0.03–0.30 mg L1 2.5–25% (v/v) NA 0.01–1 mM 2–8 g L1 20–500 mg L1 0.10–0.50% (v/v) 0.3–3.0 mM 2.0–10.0 g L1 [95] [61] [96] [31] [58] [59] [60] [61] [62] [63] Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Ethanol Glycerol Glycerol Glycerol Glycerol Glycerol Glycerol Glycerol [86] [87] [59] [88] [89] [90] [91] Ildikó V. Tóth et al. Analyte Antioxidant capacity ABTSd+ assay ABTSd+ assay ABTSd+ assay ABTSd+ assay Scavenging of H2O2 DPPHdAssay UV-Vis UV-Vis UV-Vis UV-Vis Fluorimetry UV-Vis Wine Beer, wine FIA MSFIA Chemiluminescence UV-Vis Metals and metalloids Boron Grape juice, wine MCFA Cadmium Cadmium Cadmium Cadmium Calcium Copper Copper Iron Iron Iron Iron Iron(III) Iron Iron Lead Lead Lead Lead Wine Wine Wine Wine Wine Wine Wine Beer Beer Wine Wine Wine Beer Wine Wine Wine Wine Spirits FIA FIA FIA FIA FIA FIA SIA FIA FIA SIA SIA SIA BI-FIA BI-FIA FIA FIA FIA FIA Piezoelectric microbalance FAAS CV-AAS ET-AAS ICP-OES UV-Vis FAAS FAAS FAAS UV-Vis FAAS FAAS FAAS UV-Vis UV-Vis FAAS FAAS FAAS FAAS Total phenolics Folin–Ciocalteu reducing assay 10–300 mM 4–250 mM NA 0.001–0.008 M 0.001–0.01 M 0.25 104– 6.00 104 M 1 109–5 105 M 5–80 mg L1 [41] [42] [43] [44] [44] [45] NA [97] NA r7 mg L1 r300 ng L1 r1.0 mg L1 0–350 mg L1 NA 0.20–2.00 mg L1 NA NA 0.25–15.0 mg L1 0.10–6.00 mg L1 0.25–15.0 mg L1 NA 0.1–3.0 mg L1 1.0–500 mg L1 NA 0.5–15 mg L1 5–120 mg L1 [64] [65] [66] [67] [98] [64] [99] [100] [100] [99] [101] [101] [100] [102] [68] [64] [69] [70] [46] [47] 517 FIA FIA SIA SIA SIA MSFIA Food, Beverages and Agricultural Applications Beer Wine Beer Wine Wine Beer, wine 518 Table 1 (Continued ) Matrix Flow mode Detection system Working range Reference Lead Lead Magnesium Manganese Mercury Potassium Zinc Zinc Wine Wine Wine Wine Wine Wine Beer Wine FIA FIA FIA SIA FIA SIA FIA SIA HG-AAS ICP-AES UV-Vis FAAS CV-AAS Potentiometry UV-Vis FAAS r10 mg L1 0.15–1,000 mg L1 0–350 mg L1 r3.00 mg L1 2–50 mg L1 NA NA r1.50 mg L1 [71] [72] [98] [99] [103] [104] [105] [99] FIA FIA FIA FIA FIA FIA FIA FIA FIA FIA FIA FIA FIA FIA FIA SIA FIA MCFA FIA Amperometry Amperometry Amperometry UV-Vis Fluorimetry Chemiluminescence Chemiluminescence Amperometry Amperometry Amperometry Fluorimetry UV-Vis Amperometry Fluorimetry UV-Vis UV-Vis UV-Vis UV-Vis Fluorimetry 0.2–8 mM 0.05–20 mM 3–50 mg L1 1–80 mg L1 1 106–1.6 104 M 0.1–10 mM 5–50 mM 5 106–1 103 M 0.02–1.0 mM 0.02–1.0 mM 0.05–1.5 g L1 0.1–1.0 g L1 1 105–4 104 M 0.02–1.5 g L1 0.05–1.0 g L1 0.01–0.15 g L1 0.5–4.0 g L1 0.5–10.0 g L1 0.01–1.20 mM [106] [107] [75] [108] [109] [110] [111] [32] [112] [112] [113] [113] [32] [113] [113] [114] [37] [115] [116] Organic acids and conjugate ions Acetate Wine Acetic acid Wine Ascorbic acid Wine Ascorbic acid Beer D-gluconate Noble rot wine Lactate Beer Lactate Beer L-lactic acid Wine, must D-lactic acid Beer, sake, wine L-lactic acid Beer, sake, wine L-(+)-lactic acid Wine L-(+)-lactic acid Wine L-malic acid Wine, must L-(–)-malic acid Wine L-(–)-malic acid Wine L-(–)-malic acid Wine Tartaric acid Wine Tartaric acid Wine L-tartrate Wine Ildikó V. Tóth et al. Analyte FIA HPLC-UV-Vis Anthocyan index Flavanoid fraction Flavonols (total) Wine Beer Wine FIA FIA FIA Phenolic compounds Polyphenol index Beer Wine FIA FIA UV-Vis Amperometry Adsorptive stripping voltammetry Amperometry Amperometry Polyphenol index Wine FIA UV-Vis Polyphenol index Polyphenol index Polyphenolic (three fractions) Wine Wine Wine FIA SIA FIA UV-Vis UV-Vis Evaporative light scattering Beer Must Beer, wine White wine Must Wine Wine Beer Brandy, white wine Wine Beer fermentation broth SIA FIA FIA FIA FIA FIA FIA FIA FIA FIA SIA IR Amperometry Amperometry Amperometry Amperometry Amperometry Amperometry Amperometry Chemiluminescence UV-Vis Amperometry Sugars Carbohydrates Fructose Glucose Glucose Glucose Glucose Glucose Glucose (bonded) Glucose Glucose Glucose 0.5–16 mg L1 1.0–60 mg L1 20–500 mg L1 NA 0.03–1.0 mg L1 [117] 0.025–14 mM 0.04–2.0 mg L1 (gallic acid) 0.001–0.100 mg L1 (caffeic acid) 4–22 units 3–18 units 20–70 units 5–200 mg L1 5–300 mg L1 [121] [122] 0.86–7.13 g L1 NA 2–2,500 mg L1 20–500 mg L1 NA 1 106–1 103 M 0.02–50 g L1 0.011–13.9 mM 0.0003–0.05 mM 1 106–1 103 M 5–750 mg L1 [126] [31] [127] [128] [31] [129] [130] [131] [132] [133] [35] [118] [119] [120] [123] [118] [124] [125] 519 Wine Food, Beverages and Agricultural Applications Polyphenols Anthocyanins 520 Table 1 (Continued ) Matrix Flow mode Detection system Working range Reference Maltooligosaccharides Reducing sugars Reducing sugars Beer FIA ESI-MS 5–100 mM [134] Wine Wine FIA SIA UV-Vis UV-Vis 40–400 mM 2–25 g L1 20–140 g L1 [135] [36] Sulfur dioxide Sulfite Sulfite Sulfur dioxide Sulfur dioxide Sulfur dioxide Sulfite Sulfite Sulfur dioxide Sulfur dioxide Wine, grape juice Wine Wine Wine Wine Wine White wines Wine Wine FIA FIA FIA FIA FIA FIA FIA FIA SIA Amperometry Amperometry Amperometry Amperometry Amperometry Conductimetry UV-Vis UV-Vis UV-Vis [38] [73] [74] [75] [76] [77] [78] [79] [80] Sulfur dioxide Wine MSFIA UV-Vis 1.0–5.0 mM 20–100 mM NA 0.25–15 mg L1 5–100 mM 1.0–500 mg L1 1–20 mg L1 1–200 mg L1 2–40 mg L1 25–250 mg L1 2–75 mg L1 10–250 mg L1 Sake, wine Beer Wine, must Wine Must, grape juice Beer FIA MCFA FIA FIA SIA FIA 0.2–100 mM NA 0–25 mg L1 15–60 mg L1 28–140 mg L1 NA [90] [136] [29] [30] [34] [50] Wine FIA UV-Vis Potentiometry UV-Vis UV-Vis UV-Vis Piezoelectric microbalance CE–ESI–MS NA [83] Others Acetaldehyde Acidity Ammonia Assimilable nitrogen Assimilable nitrogen Astringency and bitterness Biogenic amines [81] Ildikó V. Tóth et al. Analyte Beer FIA Body and smoothness Beer FIA Carbon dioxide Chloride Diacetyl Diacetyl Beer Wine Beer Wine FIA MSFA FIA FIA Diacetyl Diacetyl Beer Beer FIA MCFA Dissolved solids ‘‘Fingerprinting’’ ‘‘Fingerprinting’’ Histamine Laccase activity Phosphorus (total) Proline Proteins Sulfate Tannin–protein interaction Urea Urea Urea Wine Beer Beer Wine, cider Wine, must Beer Wine Rice wine Wine Wine Rice wine Wine, must Rice wine Note: NA, not given/not available. FIA FIA FIA FIA FIA FIA FIA FIA SIA FIA Piezoelectric microbalance Piezoelectric microbalance UV-Vis Potentiometry Amperometry Cathodic stripping voltammetry UV-Vis Adsorptive stripping voltammetry UV-Vis NMR MS Fluorimetry UV-Vis UV-Vis Chemiluminescence UV-Vis Turbidimetry FTIR FIA FIA FIA Fluorimetry UV-Vis UV-Vis NA [52] NA [53] 0.5–5 g L1 NA NA 1 108–1 105 M [137] [138] [55] [56] NA 5–600 mg L1 [55] [57] 0.999–1.026 g mL1 NA NA r2.0 mg L1 0.6–24.0 U mL1 2–20 mg L1 1 108–5 105 M NA 300–1,500 mg L1 NA [123] [139] [140] [141] [33] [142] [143] [144] [145] [51] 1.0–100 mM 0–25 mg L1 0.016–1.0 mM [146] [29] [39] Food, Beverages and Agricultural Applications Bitterness 521 522 Table 2 Some of the analytical features of flow methods for non-alcoholic beverages Analyte Flow mode Detection system Working range Reference Fresh fruit extracts, herbal infusions, tea Coffee, fruit juices, soft drinks, tea Fruit juices, tea Fruit juices, soft drinks, tea Fruit juices, soft drinks, tea Tea FIA Potentiometry 1 106–1 102 M [48] FIA UV-Vis 10–300 mM [41] SIA MSFIA UV-Vis UV-Vis [43] [45] MSFIA UV-Vis NA 0.25 104– 6.00 104 M 5–80 mg L1 FIA Clark-type oxygen electrode (polarography) 0.1–1.5 mM [49] Organic acids and conjugate ions Ascorbic acid Fruit juices Ascorbic acid Fruit juices L-ascorbic acid Fruit juices Ascorbic acid Fruit juices Ascorbic acid Fruit juices Ascorbic acid Fruit juices Ascorbic acid Fruit juices Ascorbic acid Fruit juices Ascorbic acid Soft drinks Ascorbic acid Fruit juices FIA FIA FIA FIA FIA FIA FIA FIA FIA FIA Amperometry Amperometry Amperometry Amperometry Chemiluminescence FAAS FAAS FAAS UV-Vis UV-Vis [75] [147] [148] [149] [150] [151] [152] [153] [108] [154] Ascorbic Ascorbic Ascorbic Ascorbic FIA FIA SIA FIA-BI UV-Vis Voltammetry Voltammetry UV-Vis 3–50 mg L1 0.025–1.0 mM 5–100 mM NA 10–1,000 mM 0.2–34.5 mg L1 0.1–50 mg L1 0.4–20 mg L1 1–80 mg L1 2.0 106– 1.0 104 M 0.3–0.8 g L1 3–35 mg L1 NA 5.1–68 mM Antioxidant capacity Total redox capacity ABTSd+ assay ABTSd+ assay DPPHd assay Folin–Ciocalteu reducing capacity Xanthine oxidase inhibitory activity acid acid acid acid Fruit Fruit Fruit Fruit juices juices juices juices [47] [137] [155] [156] [157] Ildikó V. Tóth et al. Matrix 0.1–8.0 mg L1 0.6–6.0 mM [102] [158] MPFS FIA FIA FIA FIA FIA Chemiluminescence UV-Vis Potentiometry Potentiometry Amperometry Fluorimetry r11 mM NA 1 104–1 101 M 1 103–1 101 M r20 mM 1 106–1 104 M [159] [160] [161] [162] [163] [164] FIA Fluorimetry 1 106–2 104 M [164] FIA UV-Vis NA [160] Sulfur dioxide Sulfur dioxide Sulfite Sulfite Sulfur dioxide Fruit Fruit Fruit Fruit juices juices juices juices FIA FIA FIA FIA Amperometry Amperometry Conductimetry UV-Vis 0.25–15 mg L1 20–100 mM 1.0–500 mg L1 1–200 mg L1 [75] [73] [77] [79] Fruit juices Fruit juice Tomato juice Fruit juices, soft drinks Soft drink Fruit juices Fruit juices, soft drinks Fruit juices, soft drinks Fruit juices FIA FIA FIA FIA Amperometry Amperometry Amperometry Amperometry 3–25 mM NA r100 mM r12.0 103 M [165] [166] [163] [167] FIA SIA SIA Chemiluminescence Voltammetry Chemiluminescence 0.0003–0.05 mM NA 1 105–1 103 M [132] [156] [168] MSFIA Chemiluminescence 0.090–2.7 mg L1 [169] MSFIA Chemiluminescence 2.5 106–1 103 M [170] Ascorbic acid Benzoic acid Citrate Isocitrate Lactate D-malate L-malate Sugars Fructose Glucose Glucose Glucose Glucose Glucose Glucose Glucose Glucose 523 UV-Vis UV-Vis Food, Beverages and Agricultural Applications FIA-BI MCFA Sorbic acid Fruit juices Fruit juices, soft drinks Fruit juices Orange juice Fruit juices Fruit juices Tomato juice Fruit juices, soft drinks Fruit juices, soft drinks Orange juice Ascorbic acid Ascorbic acid 524 Analyte Matrix Flow mode Detection system Working range Reference Sucrose Sucrose Fruit juices Fruit juices FIA FIA Amperometry Amperometry NA 1–12 mM [166] [165] Others Acidity Fruit juices SIA UV-Vis [171] Fruit juices Fruit juices, soft drinks Fruit juices, soft drinks SIA-LOV MCFA UV-Vis Potentiometry 0.2–1.0% (w/v) 0.5–2.5% (w/v) 0.21.2% (w/v) NA FIA Amperometry Atrazine Bitterness Orange juice Coffee FIA FIA Boron Grape juice MCFA Cadmium Cadmium Orange juice Orange juice FIA FIA Chemiluminescence Piezoelectric microbalance Piezoelectric microbalance ET-AAS CV-AAS Acidity Acidity Artificial sweeteners (acesulfame-K, cyclamate, saccharine) [172] [136] 3–30 mM (cyclamic acid) 1–10 mM (acesulfame-K) 0.3–3.5 mM (saccharin) 0.014–1.120 mg L1 NA [173] NA [97] r300 ng L1 r7 mg L1 [66] [65] [174] [52] Ildikó V. Tóth et al. Table 2 (Continued ) 10–300 mg L1 [137] 1–16 mg L1 15.0–150 mg L1 NA NA [175] [176] [177] [54] 0.1 104– 2.5 104 M 0.03–1.0 mg L1 [178] 2.0–20.0 mg L1 5.0–50 mg L1 0.001–0.01 M [176] [176] [179] UV-Vis Mass spectrometry 0.1–1.5 U mL1 NA [180] [181] UV-Vis Anodic differential pulse voltammetry UV-Vis 1–12 mg L1 3 107–1 105 M [175] [182] NA [105] Soft drinks FIA Caffeine Calcium Cations ‘‘Classification’’ Cocoa, soft drinks, tea Coconut water Fruit juices Orange juice, soft drinks Tea FIA SIA FIA FIA Evaporative light scattering detector UV-Vis UV-Vis IC-conductivity detector Potentiometry FIA Amperometry Infusions, tea, tomato juice Coconut water Coconut water Apple juice FIA SIA SIA FIA Adsorptive stripping voltammetry UV-Vis UV-Vis UV-Vis Fruit juices Grape juice FIA FIA Cocoa, soft drinks, tea Fruit juices FIA FIA Soft drinks FIA Flavonoids Flavonols (total) Iron Magnesium Organophosphate pesticides Pectinesterase activity Proanthocyanidins oligomers Theobromine Tin Zinc Note: NA, not given/not available. [120] Food, Beverages and Agricultural Applications Caffeine 525 526 Table 3 Some of the analytical features of flow methods for fruits and vegetables Matrix Flow mode Detection Working range or LOD Reference Pesticides Bitertanol Carbamate Carbaryl Carbaryl Fruit, banana Vegetables Vegetables Vegetables MCFA FIA FIA FIA Fluorimetry UV-Vis Chemiluminescence Chemiluminescence [189] [202] [194] [196] Carbaryl Carbaryl Fruits Vegetables FIA FIA ESI(MS/MS) UV-Vis Carbofuran Carbofuran Fruits, vegetables Vegetables MSFA FIA Amperometry Chemiluminescence Chlorpyrifos Fruits FIA Chemiluminescence Dimethylarsinic Dimethoate Vegetables Vegetables FIA SIA Fluorimetry UV-Vis Diphenylamine Fruits MCFA Fluorimetry Dichlorvos Vegetables FIA Chemiluminescence DDVP 2,4-D Imazalil Malathion Malathion Methamidophos Fruits Fruits Citrus fruits Grains, vegetables Fruits Vegetables FIA FIA FIA FIA FIA FIA ESI(MS/MS) ESI(MS/MS) ESI(MS/MS) Fluorimetry ESI(MS/MS) Fluorimetry LOD: 0.014 mg kg1 LOD: 3.5–25mg L1 30–100 mg L1 5–100 ng mL1, LOD: 4.9 ng mL1 0.002–5.0 mg g1 LOD: 0.4 ng LOD: 25 ng 109–107 M 0.06–0.5 mg mL1 LOD: 0.02 mg mL1 0.48–484 ng mL1 LOD: 0.18 ng mL1 LOD: 0.014 mg mL1 0.03–0.5 mg g1 LOD: 0.01 mg g1 0.25–5 mg kg1 LOD: 0.06 mg kg1 0.02–3.1 mg mL1 LOD: 0.008 mg mL1 0.002–5.0 mg g1 0.002–5.0 mg g1 0.2–5 mg mL1 20–2,000 ng mL1 0.002–5.0 mg g1 14–1,400 ng mL1 LOD: 1.7 ng mL1 [192] [200] [203] [195] [198] [183] [201] [188] [197] [192] [192] [191] [185] [192] [184] Ildikó V. Tóth et al. Analyte Methylcarbamates N-methylcarbamate o-Phenylphenol Propoxur Fruits, vegetables FIA-LC Fluorimetry LOD: 3–12 ng g1 [186] Fruits Citrus fruits Vegetables FIA FIA FIA ESI(MS/MS) ESI(MS/MS) UV-Vis [193] [191] [199] Propoxur Vegetables FIA UV-Vis Organophosphorus Organophosphorus Vegetables, grains Vegetables FIA-HPLC SIA Fluorimetry UV-Vis Organophosphorus Thiabendazole Vegetables Fruits FIA MCFA UV-Vis Fluorimetry Thiabendazole Citrus fruits FIA ESI(MS/MS) 0.01–0.7 mg mL1 0.4–10 mg mL1 1–10 mg L1 LOD: 0.15 mg L1 LOD: 0.4 ng LOD: 25 ng LOD: 4–12 ng mL1 0.03–0.5 mg g1 LOD: 0.01 mg g1 LOD: 3.5–25 mg L1 0.3–10 mg kg1 LOD: 0.09 mg kg1 0.4–10 mg mL1 Toxins Aflatoxin B1 Aflatoxin B1 Fumonisin B1 Fumonisin B1 Ochratoxin A Fruits Barley, wheat Corn products Corn Barley, wheat SIA-immuno FIA FIA-immuno FIA-immuno FIA UV-Vis UV-Vis UV-Vis UV-Vis UV-Vis LOD: 0.2 ng mL1 0.5–10 ng mL1 NA 1–1,000 ng mL1 0.5–10 ng mL1 [215] [216] [217] [218] [216] Inorganic anions Chloride Nitrite/nitrate Coconut water Vegetables FIA FIA Potentiometry UV-Vis [219] [220] Nitrite/nitrate Nitrite/nitrate Vegetables Vegetables FIA FIA UV-Vis FAAS 4–1,000 mg L1 0.30–3.00 mg L1 (NO 2) 1.00–10.00 mg L1 (NO 3) LOD: 2.96 mg r20 mg L1 (NO 2) LOD: 0.07 mg L1 r30 mg L1 (NO 3) 0.14 mg L1 [200] [190] [201] [202] [187] [221] [222] Food, Beverages and Agricultural Applications [191] 527 528 Table 3 (Continued ) Matrix Flow mode Detection Working range or LOD Reference Nitrate Nitrite Vegetables Flour, wheat FIA FIA UV-Vis Potentiometry [223] [224] Nitrate Orthophosphate Vegetables Cereals SIA FIA UV-Vis UV-Vis 1.00–10.00 mg L1 1.0 106– 1.0 101 M 1.35–50 mg L1 r196 106 (P) M Carbohydrates Fructose Fruits FIA Voltammetry [227] Glucose Fruits FIA Voltammetry Starch Flour, bread FIA UV-Vis r60 mM, LOD: 1.2 mM r60 mM, LOD: 1.2 mM 0.05–9 g L1 Organic acids and conjugate ions Ascorbic acid Fruits, vegetables Ascorbic acid Vegetables Ascorbic acid Vegetables Ascorbic acid Vegetables Ascorbic acid Vegetables, fruits Oxalic acid Vegetables FIA FIA FIA FIA FIA FIA Turbidimetry Chemiluminescence FAAS FAAS Fluorimetry UV-Vis Oxalic acid Vegetables FIA UV-Vis Oxalate Vegetables FIA Chemiluminescence Phytic acid Pyruvate Plant Onion MPFS FIA UV-Vis UV-Vis LOD: 1 mg mL1 LOD: 1 1013 M 0.1–50 mg L1 0.3–60 mg mL1 LOD: 0.012 mg mL1 0.1–8.0 mg mL1 LOD: 0.04 mg mL1 0.1–8.0 mg mL1 LOD: 0.08 mg mL1 2 106–9.5 105 M LOD: 0.05 mg mL1 LOD: 1 mg L1 NA [225] [226] [227] [228] [204] [207] [152] [205] [206] [229] [230] [231] [232] [233] Ildikó V. Tóth et al. Analyte Metals and metalloids Aluminium Arsenic Boron Crystallized fruits Seaweed Plant Cadmium Cadmium Cadmium Cadmium LOD: 0.1–0.8 mg L1 NA LOD: 0.05 mg mL1 [234] [235] [236] UV-Vis FAAS CV-AAS FAAS [237] [238] [65] [239] Cadmium Copper Germanium Gold Lead Lead Lead Mercury Molybdenum Nickel Nickel Powdered corn Plant Mung bean, kelp Apple leaves Vegetables Powdered corn Corn Vegetables Mung bean, kelp Plants, flour Plants FIA MCFA FIA FIA FIA FIA FIA FIA FIA FIA FIA UV-Vis FAAS UV-Vis ICP-MS TS-FF-AAS UV-Vis ICP-MS CV-AAS UV-Vis FAAS UV-Vis Selenium Cereals, bakery products Apple leaves Apple leaves Mung bean, kelp Apple leaves Powdered corn Corn FIA HG-GFAAS 5–50 mg L1 LOD: 0.014 mg g1 LOD: 0.02–0.40 mg g1 LOD: 0.014– 0.011 mg g1 0.05–3.0 mg mL1 LOD: 1 ng mL1 NA LOD: 0.64 pg mL1 5.2–300.0 mg L1 0.05–6.0 mg mL1 NA LOD: 0.86 mg L1 NA 5–250 mg L1 0.05–0.50 mg L1 LOD: 17 mg L1 LOD: 0.06 mg L1 FIA FIA FIA FIA FIA FIA ICP-MS ICP-MS UV-Vis ICP-MS UV-Vis ICP-MS LOD: 0.82 pg mL1 LOD: 2.24 pg mL1 NA LOD: 0.05 pg mL1 0.05–2.0 mg mL1 NA [243] [243] [242] [243] [240] [245] Silver Tellurium Tin Uranium Zinc Zinc [240] [241] [242] [243] [244] [240] [245] [246] [242] [247] [248] [249] Food, Beverages and Agricultural Applications UV-Vis HG-AAS UV-Vis Vegetables Vegetables, fruits Vegetables Legumes, fruits MCFA FIA Continuous flow MCFA FIA FIA FIA 529 530 Analyte Matrix Flow mode Detection Working range or LOD Reference Others Antioxidant capacity Antioxidant capacity Antioxidant capacity Antioxidant capacity Formalin Glucosinolate Fruits, vegetables Vegetables Herbs Vegetables Fruits Vegetables FIA FIA FIA FIA FIA FIA Amperometry Amperometry Amperometry Chemiluminescence Amperometry Amperometry 0.1–0.5 mM 1.0–10 mg L1 NA NA LOD: 0.0129 mM 0.005–1.0 mM LOD: 0.002 mM NA LOD: 75 mM [49] [208] [209] [210] [250] [213] r196 106 (P) M 0.09–45.0 mg mL1 LOD: 0.05 mg mL1 r196 106 (P) M 1.6 ng mL1 [226] [214] b-Glucan myo-inositol phosphate Phosphorus (total) Phylloquinone Oat Fruits, legumes FIA FIA-CE Fluorimetry UV-Vis Cereals Vegetables, fruits FIA FIA UV-Vis Fluorimetry Phytate Synephrine Cereals Herbs, fruits FIA FIA UV-Vis Chemiluminescence Note: NA, not given/not available. [251] [212] [226] [211] Ildikó V. Tóth et al. Table 3 (Continued ) Table 4 Some of the analytical features of flow methods for milk and dairy samples Analyte Matrix Flow mode Detection Working range or LOD Reference Antibiotics Gentamicin Nafcillin Oxytetracycline Milk Milk Milk FIA-immuno FIA FIA Amperometry Phosphorescence Voltammetry [254] [256] [255] Streptomycin Tetracycline Milk Milk FIA LOV Chemiluminescence Chemiluminescence LOD: 100 mg kg1 LOD: 3.6 107 M 100 ng mL1 200 ng g1 LOD: 5.16 109 M LOD: 2.0 mg L1 Inorganic anions Chloride Chloride Chloride Nitrate/nitrite Nitrite Milk Milk Milk Dairy Milk FIA SIA MSFA SIA FIA Potentiometry Potentiometry Potentiometry UV-Vis Potentiometry Nitrite Milk, cheese FIA Potentiometry Milk Milk Milk Milk Milk Dairy products Pasteurized milk, buttermilk, lowlactose milk FIA FIA FIA FIA FIA FIA FIA Amperometry Amperometry Amperometry Amperometry Amperometry Amperometry Amperometry 4–1,000 mg L1 0.01–0.25 M NA LOD: 0.15 mg L1 1.0 106– 1.0 101 M 1.0 106– 1.0 101 M [219] [281] [138] [272] [273] LOD: 0.1 mM 0.1–20 mM LOD: 0.2 mM LOD: 0.1 mM 0.05–10 mM LOD: 0.06 mM 1–100 mM [165] [277] [165] [165] [277] [280] [274] [224] Food, Beverages and Agricultural Applications Sugars Fructose Galactose Galactose Glucose Glucose Glucose Lactose [257] [258] 531 Matrix Flow mode Detection Working range or LOD Reference Lactose Lactose Cheese whey Milk and instant dessert powder Milk Milk Different types of milk FIA FIA Amperometry Amperometry 1–30 g L1 LOD: 0.5 mM [275] [279] FIA FIA FIA Amperometry Amperometry Amperometry [165] [277] [276] Milk-based and sugar candidate artificial certified reference materials (CRMs) Milk-based and sugar candidate artificial CRMs FIA UV-Vis LOD: 0.8 mM 0.2–20 mM 3.0 105– 1.0 103 M LOD: 9.6 106 M 0.01–0.80% (w/v) FIA UV-Vis 0.01–0.80%(w/v) [278] MCFA FIA HG-AFS AAS LOD: 1.67 ng g1 LOD: 0.014 mg g1 [260] [261] FIA FIA UV-Vis Electrochemiluminescence [240] FIA FIA Potentiometry FAAS 0.05–3.0 mg mL1 8.0 106 to 1.0 104 M LOD: 2.0 106 M 104–102 M LOD: 2.5 mg L1 [262] [263] [264] FIA ICP-MS LOD: 0.64 pg mL1 [243] Lactose Lactose Lactulose Monosaccharides Oligosaccharides Metals, metalloids Bismuth Cadmium Cadmium Calcium Calcium Chromium (III) Gold Milk shakes Solid and semisolid milk Milk powder Milk Whole milk Non-fat milk powder Milk powder [278] Ildikó V. Tóth et al. Analyte 532 Table 4 (Continued ) Iron Iron Lead Manganese(II) Milk powder, infant formula Milk FAAS LOD: 0.60 mg g1 [265] Closed-loop FIA FIA FIA UV-Vis NA [266] UV-Vis FAAS 0.05–6.0 mg mL1 LOD: 1.1 mg L1 [240] [264] MCFA CV-AFS LOD: 0.011 ng g1 [267] FIA MCFA MCFA FIA FIA FIA ICP-MS HG-AFS HG-AFS ICP-MS ICP-MS FAAS LOD: LOD: LOD: LOD: LOD: LOD: FIA FIA UV-Vis ICP 0.05–2.0 mg mL1 Various [240] [271] [252] [253] [282] Others Aflatoxin M1 Milk FIA-immuno Amperometry Aflatoxin M1 Antioxidant activity Antioxidant activity Choline Cheese Milk FIA FIA Amperometry Amperometry 20–500 ppt LOD: 11 ppt Subnanomolar NA Milk FIA Amperometry NA [283] Milk FIA Potentiometry [284] Choline Milk FIA Amperometry 5.0 104– 5.0 103 M r0.5 mM Mercury Silver Tellurium Tellurium Tellurium Uranium Zinc 0.82 pg mL1 0.57 ng g1 0.20 ng L1 2.24 pg mL1 0.05 pg mL1 0.3 mg g1 [243] [269] [268] [243] [243] [270] [285] 533 Zinc Various metals Milk powder Non-fat milk powder Milk, non-fat milk powder Milk powder Milk Milk Milk powder Milk powder Milk powder, infant formula Milk powder Powdered milk Food, Beverages and Agricultural Applications FIA 534 Analyte Matrix Flow mode Detection Working range or LOD Reference Choline Milk, milk powder, soy lecithin Milk FIA Amperometry NA [286] FIA 0.20–0.45% (w/v) [287] FIA FIA FIA FIA Piezoelectric microbalance Chemiluminescence Amperometry Amperometry Amperometry LOD: 0.35 mg mL1 r50 mM 10–180 mM LOD: 4 mM [288] [289] [290] [291] FIA FIA SIA SIA SIA Amperometry UV-Vis UV-Vis Conductimetry UV-Vis LOD: LOD: LOD: LOD: LOD: [280] [292] [293] [294] [294] Fat matter Isoniazid Lactate Lactate Lactate Lactate Phosphorus Phosphorus Urea Urea Milk Milk and yoghurt Dairy products Fermentation monitor Dairy products Milk Milk Milk Milk Note: NA, not given/not available. 0.1 mM 2 mg L1 2 mg L1 2.6 104 M 2.8 105 M Ildikó V. Tóth et al. Table 4 (Continued ) Table 5 Some of the analytical features of flow methods for meat and fish products Analyte Matrix Flow mode Detection Working range or LOD Reference FIA UV-Vis LOD: 0.05 mg L1 [297] Nitrite/nitrate Nitrite/nitrate Frankfurter and dry sausages Cured meat Meat SIA FIA UV-Vis UV-Vis [298] [220] Nitrite/nitrate Fish FIA UV-Vis Nitrite Nitrite Meat Sausage FIA FIA UV-Vis Potentiometry Nitrite Sausage FIA Potentiometry Nitrite Nitrate Meat Meat Continuous flow FIA UV-Vis UV-Vis LOD: 9 mg L1 LOD: 13 and 20 mg kg1 LOD: 0.01 and 0.025 mg mL1 LOD: 7.5 mg mL1 1.0 106– 1.0 101 M 1.0 106– 1.0 101 M 0.1–50 mg L1 LOD: 2.97 mg Metals, metalloids Arsenic Arsenic Arsenic Cadmium Fish Fish Seafood Meat FIA FIA FIA FIA HGAAS HGAAS HG-ETAAS FAAS Cobalt Fish and eggs FIA Chemiluminescence Cobalt Bovine liver, fish, mussel Pork liver Meat Fish MSFA FIA FIA FIA Inorganic anions Nitrite [295] [273] [224] [299] [221] UV-Vis FAAS FAAS NA LOD: 0.6 mg g1 LOD: 0.8 mg L1 [242] [332] [333] [330] [331] 535 [326] [327] [328] [329] UV-Vis LOD: 045 mg g1 LOD 0.34 mg L1 LOD 72.1 ng L1 LOD: 0.014 mg 60 mg1 10 fg mL1 to 50 pg mL1 LOD: 1.66 ng L1 Food, Beverages and Agricultural Applications Germanium Iron Lead [296] 536 Table 5 (Continued ) Matrix Flow mode Detection Working range or LOD Reference Lead Mercury Mercury Mercury Mercury Mercury Mercury Mercury Mercury Mercury Mercury Molybdenum Selenium Selenium Tin Zinc Various metals FIA MCFA FIA MSFIA FIA FIA-HPLC FIA-LC FIA FIA FIA SIA FIA FIA FIA FIA FIA FIA FAAS CVAAS CVAAS CVAAS VGAAS UV ETAS CVAAS CVAAS CVAAS CVAAS UV-Vis Amperometry HGAAS UV-Vis FAAS ICP LOD: 1.0 mg L1 LOD: 4.8 mg kg1 LOD: 4–26 ng g1 LOD: 5 ng L1 LOQ: 55 ng g1 LOD: 10–25 ng g1 LOD: 6.8 ng L1 LOD: 57 ng g1 LOQ: 0.86 mg L1 NA LOD: 0.46 mg L1 NA LOD: 6 mg L1 LOD: 10 mg L1 NA LOD: 0.6 mg g1 Various [334] [309] [308] [307] [306] [305] [304] [303] [246] [335] [302] [242] [336] [337] [242] [338] [271] Various metals Seafood Fish Fish Fish Fish Seafood Fish Fish Fish, seafood Seafood Fish Pork liver Fish Dry fish Pork liver Meat Bovine liver, mussel tissue Fish liver FIA ETV-ICP-MS Various [339] Quality indicators Agmatine Biogenic amines Histamine Histamine Histamine Histidine Putrescine Trimethylamine Fish Fish, meat sausage Fish Fish Fish Fish Fish Fish FIA FIA-CE FIA FIA FIA FIA FIA FIA Amperometry Amperometry Amperometry Fluorimetry Amperometry Chemiluminescence Amperometry Amperometry LOD: 0.005 mM LOD: 0.2–0.6 mg mL1 LOD: 100 pmol LOD: 0.8 mg kg1 LOD: 2.2 mM LOD: 0.01 mM LOD: 5 mM 1.0–50.0 mM [314] [322] [310] [311] [312] [340] [313] [315] Ildikó V. Tóth et al. Analyte Escherichia coli O15 Others Nitrosamine Nitrosodimethylamine Oxytetracycline Tetracycline Seafood Fish Fish, hake Fish sauce Fish Fish, hake FIA FIA FIA FIA FIA FIA Potentiometry UV-Vis UV-Vis UV-Vis UV-Vis UV-Vis LOD: 0.05 mg mL1 NA 0.3–7 mg N L1 50–200 mM (N) NA 1.4–14 mg N L1 [316] [341] [318] [317] [342] [318] Fish sauce FIA UV-Vis 50–500 mM (N) [317] Fish FIA UV-Vis NA [342] Fish Meat Meat FIA FIA FIA Amperometry Amperometry Amperometry LOD: 2–3 107 M NA 2 106–2 103 M [321] [320] [343] Poultry FIA Amperometry 105 CFU mL1 [324] Poultry FIA Piezoelectric microbalance [323] Poultry FIA Amperometry 107–109 CFU mL1 or 106–1,010 CFU mL1 LOD: 6 102 cell mL1 Cured meat Cured meat FIA FIA UV-Vis Chemiluminescence 0.8–2,000 ng mL1 LOD: 0.29 ng mL1 [301] [300] Eggs Fish FIA FIA Voltammetry Chemiluminescence NA 4 109– 4 107 g mL1 [255] [344] 537 Note: NA, not given/not available. [325] Food, Beverages and Agricultural Applications Trimethylamine Trimethylamine Trimethylamine Trimethylamine Trimethylamine Total volatile basic nitrogen Total volatile basic nitrogen Total volatile basic nitrogen ‘‘Freshness’’ ‘‘Freshness’’ ‘‘Freshness’’ (hypoxantine/ polyamines) Salmonella typhimurium S. typhimurium 538 Table 6 Some of the analytical features of flow methods for food analysis, miscellaneous food products Analyte Flow mode Detection Application range or LOD Reference Sugars Monosaccharides/ oligosaccharides Fructose Glucose Honey, syrups FIA UV-Vis 0.01–0.80% (w/v) [278] Syrup Honey MPFS FIA UV-Vis Chemiluminescence [378] [132] Glucose Glucose Glucose Oily food Syrup Honey FIA MPFS SIA Amperometry UV-Vis Chemiluminescence Glucose Honey SIA/FIA Chemiluminescence 0.50–2.00% (w/v) 3 104– 5 102 mM 0–1.0 mM 0.50–2.00% (w/v) 1 105– 1 103 M, LOD: 1 106 M 0.01–1 mM, LOD: 4 mM FIA HG-AAS LOD: 0.068 mg kg1 [380] FIA HG-AAS LOD: 0.15 mg kg1 [380] Boron Foods (daily food intake) Foods (daily food intake) Vinegar MCFA NA [97] Cadmium Cobalt Copper Honey Honey Vegetable oil FIA FIA FIA Piezoelectric microbalance FAAS FAAS FAAS LOD: 0.5 ng g1 LOD: 0.18 mg L1 NA [356] [357] [348] Metals and metalloids Arsenic Antimony [379] [378] [168] [355] Ildikó V. Tóth et al. Matrix Iron Lead Selenium SIA FIA FIA UV-Vis FAAS HG-AAS LOD: 0.31 mg LOD: 350 ng g1 LOD: 0.060 mg kg1 [349] [369] [380] Zinc Various metals Edible oil Sweeteners Foods (daily food intake) Vegetable oil Oil FIA FIA FAAS ICP-MS, FAAS NA Various [348] [347] Artificial sweeteners Acesulfame-K Sweetener FIA UV-Vis [374] Acesulfame-K Aspartame Sweetener tablets Sweeteners FIA FIA Amperometry UV-Vis Aspartame FIA UV-Vis FIA UV-Vis 10–200 mg mL1 [372] Aspartame Low-calorie dietary products Low-calorie dietary products Sweetener tablets 40–100 mg mL1, LOD: 11.9 mg mL1 1–10 mM 10–80 mg mL1, LOD: 4 mg mL1 5–600 mg mL1 SIA Chemiluminescence [373] Aspartame Sweetener FIA UV-Vis Cyclamate Sweetener FIA Turbidimetry Cyclamate Sweetener FIA UV-Vis Cyclamate Sweetener tablets FIA Amperometry r350 mg L1, LOD: 2.16 mg L1 10–100 mg mL1, LOD: 5.65 mg mL1 0.015–0.120%(w/v), LOD: 0.006% (w/v) r3.0 mM, LOD: 30 mM 3–30 mM [371] [374] [375] [376] Food, Beverages and Agricultural Applications Aspartame [173] [370] [173] 539 540 Table 6 (Continued ) Matrix Flow mode Detection Application range or LOD Reference Saccharine Saccharine Sweetener tablets Low-calorie dietary products FIA FIA Amperometry UV-Vis 0.3–3.5 mM 10.0–200.0 mg mL1 [173] [372] Vinegar Sweetener Honey FIA FIA FIA Conductimetry UV-Vis UV-Vis [77] [377] [42] Honey FIA Amperometry 0.010–0.100 M r103 M 4–250 mM, LOD: 1.3 mM NA Honey, propolis, royal jelly Sweets Bread FIA Amperometry NA [359] FIA-BI FIA UV-Vis UV-Vis [157] [345] Chocolate Soup Pasta Lard, butter, pasta Sweetener Dehydrated broths FIA FIA FIA FIA FIA FIA UV-Vis Chemiluminescence UV-Vis Amperometry UV-Vis UV-Vis 5.1–68 mM 2 106– 2.1 105 M, LOD: 8 107 M 1–16 mg L1 0.02–0.12 (OD600) 0–2 mg mL1 0.1–0.5 mM r103 M 0.342–1.368 mg 100 mL1, LOD: 0.185 mg 100 mL1 Others Acetic acid Aniline Antioxidant activity Antioxidant activity Antioxidant activity Ascorbic acid Bromate Caffeine Catalase activity Cholesterol Cholesterol Cyclohexylamine Creatinine [358] [175] [365] [361] [346] [377] [366] Ildikó V. Tóth et al. Analyte Glucose Honey, vinegars Soup FIA FIA Fluorimetry Amperometry Hydroxyl radicals Oil FIA Fluorimetry Iodine value Lipid hydroperoxide Lysine Olive oil Oil FIA FIA UV-Vis Chemiluminescence 106–1.6 104 M 0.1–15.5 mM, LOD: 0.08 mM 2.6 107– 4 105 M, LOD: 7.91 108 M 9–125 IV NA Hydrolysate food samples Tomato paste, baby food Soup-formulas Food seasonings FIA Amperometry 1 103–5 105 M [381] FIA Amperometry 0–0.1 nM [382] FIA FIA Potentiometry Amperometry [383] [362] Soup FIA UV-Vis 2.5–75 mM 10–160 mg L1, LOD: 1.7 mg L1 r140 mM, LOD: 1 mM Monosodium glutamate Oligomeric proanthocyanidin Soup FIA Amperometry [368] Health foods FIA UV-Vis Parabens Propyl gallate Soysauce Dehydrated broth bar, olive oil FIA FIA Chemiluminescence Amperometry 0.1–15.5 mM, LOD: 0.08 mM 0.010– 0.20 mg mL1, LOD: 5 mg mL1 Various 9 107– 1.1 106 M D-gluconate L-lactate [350] [351] [352] [367] [384] [363] [353] Food, Beverages and Agricultural Applications L-glutamate Monosodium glutamate Monosodium glutamate [109] [368] 541 542 Analyte Matrix Flow mode Detection Application range or LOD Reference P4R and N2N (dyes) Sweets FIA Solid-phase UV-Vis [385] Synthetic antioxidants Sudan I Tetracycline Fat foods UV-Vis Hot chilli sauce Honey Continuous flow FIA FIA-HPLC 0.30–20 mg L1 (P4R) 0.02–3.0 mg L1 (N2N) 10–300 mg mL1 Chemiluminescence Chemiluminescence Chocolate Oil FIA FIA Oily food FIA Theobromine Total lipid hydroperoxides Water Note: NA, not given/not available. [386] [364] [360] UV-Vis Fluorimetry LOD: 3 pg mL1 LOD: 0.9– 5.0 ng mL1 1–12 mg L1 NA Amperometry NA (0–65%) [387] [175] [354] Ildikó V. Tóth et al. Table 6 (Continued ) Food, Beverages and Agricultural Applications 543 The analysis of alcoholic beverages can be a cumbersome process because ethanol can be a serious interferent in almost all detection systems. Calibration using standards containing ethanol is a frequent solution to this problem [36,37], but this then requires that the application be devised for a specific matrix, considering that beer, wine or spirits have very different ethanol content. The ethanol interference may be circumvented by performing a ‘‘blank’’ measurement, as suggested by Corbo et al. [38] for the amperometric determination of sulfite. Since ethanol is electroactive and also permeates through the gasdiffusion membrane, the pH of the donor stream was changed to provide an analytical signal proportional to the ethanol present in the wine samples, which was then subtracted from the signal corresponding to sulfite plus ethanol. Another strategy suggested by Iida et al. [39] involved the application of a hollow-fibre membrane containing a non-porous layer at its outer surface for selective diffusion of carbon dioxide in the enzymatic determination of urea. The analytes presented in Tables 1 and 2 illustrate the dual role that flowbased methods have in food analysis. These include flow systems devised for routine analysis, based on well-established methods. However, flow systems were also applied to novel analytical tasks, such as the determination of analytes related to sensory properties or to characteristics that contribute to a value-added product. Antioxidants belong to this last class of compounds, and there has recently been an increased demand for methods to assess the ‘‘antioxidant properties’’ or the ‘‘antioxidant capacity’’ of food products [40]. These methods include the evaluation of either the generic ‘‘reduction’’ capacity, or the determination of a specific analyte (ascorbic acid, vitamin E) or class of analytes (phenolic compounds, carotenoids). The most common of these methods is based on the scavenging of a coloured radical, namely 2,2u-azinobis(3-ethylbenzothiazoline-6sulfonic acid) (ABTSd+) or 2,2-diphenyl-1-picrylhydrazyl (DPPHd). The automation of these assays is definitely advantageous, as reported by several authors [41–49], because strict control of reaction time and media composition are necessary to achieve repeatable and comparable results. Labrinea and Georgiu [42] reported the use of gradient calibration to perform automated dilution of concentrated samples and to obtain analytical measurements at different assay times. In this way, information concerning the kinetics of ABTSd+ bleaching was obtained after a single injection. For the same assay, studies concerning the influence of pH (5.4, 7.4 or unbuffered) were carried out in an SIA system [43]. A thorough mixture between food samples, buffer and ABTSd+ was attained in a mixing chamber placed in a lateral port of the selection valve. Comparing the two endpoint batch method protocols, the DPPHd assay takes considerably more time (up to 2 h) than the ABTSd+ assay (10–30 min). In the multisyringe flow-injection analysis (MSFIA) system proposed by Magalhães et al., a stopped flow approach was adopted, and the data collected within the first 3 min of reaction was used to calculate the total DPPHd consumption for samples containing slow reacting compounds [45]. The results were comparable to those attained using the endpoint batch method, with a considerable reduction of the analysis time. The multi-channel features of the selection valve used in SIA were exploited to 544 Ildikó V. Tóth et al. implement two complementary determinations. The reagent reservoirs (ABTSd+, H2O2 and homovanylic acid) and detectors (spectrophotometer and fluorimeter) were connected to different lateral ports of the selection valve. In accordance with the routine protocol, the scavenging activity was measured against either ABTSd+ or H2O2 [44]. The application of flow systems to the determination of parameters that can be correlated to astringency [50,51], bitterness [52], body and smoothness [53] has also been reported. These reports illustrate how useful flow injection-based techniques are for these types of studies. The functioning of an FIA manifold can mimic that which occurs in the mouth, where the sensory receptors (similar to the detector in a flow system) are constantly washed by saliva (carrier). The sensory stimulus is equivalent to the sample plug dispersed in the carrier in a flowinjection system, which has a transient effect on the receptor because it is continuously rinsed by fluids. For example, Kaneda and co-workers applied a lipid-coated quartz crystal microbalance connected to a flow-injection system to simulate and study the electrostatic and/or hydrophobic interactions of the beer taste components with the tongue and throat surfaces [50,52,53]. The results obtained showed a good correlation with those obtained from a sensory evaluation panel. The implementation of a potentiometric sensor array in an FIA system to distinguish simple tastes and to classify food samples has also been described [54]. Furthermore, systems for determination of specific analytes that contribute to the typical sensory characteristics of wine and beers have been proposed. Worthy of mention are manifolds for the determination of diacetyl [55–57], a strong smelling compound that evokes a buttery aroma, and glycerol [31,58–63] that is used to confer smoothness to wine. New flow-based methodologies for routine monitoring of food safety aspects should be highlighted. These include systems proposed for the determination of heavy metals, such as cadmium [64–67] and lead [64,68–72]. In almost all of these proposed manifolds, in-manifold complexation of the target metal, followed by in-manifold solid-phase extraction, elution and detection by atomic or emission spectrometry was adopted. Chuachuad and Tyson adopted another strategy by using immobilized tetrahydroborate to generate volatile species of cadmium [65] or lead [71], which were further determined by chemical vapour–atomic absorption spectrometry. Sulfur dioxide, added as a preservative to wine and fruit juices, may cause allergic response in susceptible subjects. Besides the mandatory indication of its presence in many countries, its levels are defined by legislation [3]. Several automatic systems have been proposed for determination of this analyte [38,73–81], with most incorporating some sort of gas diffusion device to allow the separation of SO2 from the food matrix before direct electrochemical detection [38,73,75,77] or before further derivatization and spectrophotometric detection [78–81]. The automatic assessment of ethyl carbamate precursors has also been described. High levels of urea and assimilable nitrogen at the initial stages of wine production are related to the content of potentially carcinogenic ethyl carbamate in the final product [82]. Therefore, Gonzalez-Rodriguez et al. Food, Beverages and Agricultural Applications 545 proposed an FI–pervaporation system for monitoring urea and ammonia [29]. The automatic determination of assimilable N using a similar strategy [30] or an SIA system [34] is also possible. The application of FIA as a sample-handling tool is highlighted in the determination of biogenic amines by capillary electrophoresis–electrospray mass spectrometry [83]. In this case an FIA system was used to perform in-manifold filtration and exact volume delivery to vials placed at an automatic sampler. 3. APPLICATIONS: PLANTS AND VEGETABLES Modern agricultural economies are highly dependent on the use of pesticides. A pesticide is defined as any substance or mixture of substances intended for preventing, destroying, repelling or mitigating any pest. Pests can be insects, mice and other animals, unwanted plants (weeds), fungi or other microorganisms. Substances or mixture of substances intended for use as a plant regulator, defoliant or desiccant can also be considered as pesticides. Therefore, a large and ever increasing variety of more than 1,000 of these compounds can be applied to agricultural crops during plant development, post-harvest processing and transport. These compounds might potentially remain in foodstuffs, resulting in an elevated risk, especially in freshly consumed fruits and vegetables. Regulatory bodies [1–3] have established various maximum residue limits for pesticides in foodstuffs. The determination of these residue levels in vegetables and fruits is a difficult task, not only because of the low target concentrations, but also due to complexity and variety of the samples. For these reasons analytical procedures with high selectivity and sensitivity are required. In the last few years various analytical methods based on flow techniques have been developed for these purposes (Table 3). Flow systems exploiting the selectivity and sensitivity of fluorimetric detection for the determination of pesticides have been developed [183–189], with one even incorporating an automated separation of organophosphorus pesticides by high-performance liquid chromatography (HPLC) followed by flow-injection post-column derivatization [190]. Tandem mass spectrometry (MS/MS) has been used in conjunction with flow injection methods to solve various analytical problems in biological samples. The hyphenation with flow injection enables both the quantitative and qualitative analysis of certain analytes and complex mixtures with little or no clean-up procedure. The first of the tandem MS detectors is used to detect separated compounds from all of the ionized compounds based on mass differences, while the second is used for detection of the target analyte, representing an advantageous alternative for analysis of pesticide residues [191–193]. Other flow methods can make use of chemiluminometric [194–198], spectrophotometric [199–202] or electrochemical [203] detection systems. The advantages associated with the consumption of vegetables and fruits are well-known. The nutritional quality of these products is of great interest to consumers and producers, and the assessment of nutritional parameters using flow methods has received increased attention since the first applications of these 546 Ildikó V. Tóth et al. methods were published. These parameters continue to be the centre of attention as demonstrated by the various papers published on the determination of ascorbic acid [152,204–207], total antioxidant capacity [49,208–210] and other components with specific nutritional or health benefits [211–214]. The flow methods developed for the determination of ascorbic acid were based on the reducing capacity of the analyte [152,205,206], with detection limits of as little as 1013 M being achieved by signal enhancement of the chemiluminescence reaction of cerium(IV) with Rhodamine B [207]. The antioxidant capacity assays, described earlier for the beverage samples, in many cases can be applied with little modification to the analysis of fruits and vegetable extracts. Besides the nutritional value of vegetable and fruit products, their inherent ability to accumulate potentially harmful substances such as nitrates and heavy metals has also received considerable attention and this is reflected in the number of papers dealing with these analytes. 4. APPLICATIONS: MILK AND DAIRY PRODUCTS The aflatoxins are a group of structurally related toxic compounds produced by certain strains of fungi. Under favourable conditions of temperature and humidity, these fungi grow on certain foods and feeds, resulting in the production of aflatoxins. These toxins can be found in combination in various foods and feeds in various proportions; aflatoxin B1, however, is usually predominant and is the most toxic [3]. Aflatoxin M1 (AFM1) is the major metabolic product of aflatoxin B1 in animals and is usually excreted into the milk of dairy cattle and other mammalian species that have consumed aflatoxin-contaminated food or feed. The food and drug administration (FDA) has set the action levels for aflatoxins at 20 mg kg1 in all food products designated for humans, other than milk; in milk this level is lowered to 0.5 mg kg1. However the current maximum level set by the European Union is 0.05 mg kg1 for AFM1 in milk [2,3]. Thus, concerns about sampling, sample preparation and analysis still remain in focus when determination of aflatoxins at the parts-per-billion level is to be reached. Immunochemical flow methods have been developed on the basis of the highly specific affinities of monoclonal or polyclonal antibodies for aflatoxins assays. Badea et al. [252] have developed a flow-injection immunoassay system for the determination of AFM1 in raw milk, establishing a dynamic concentration range between 20 and 500 ppt AFM1, with a detection limit of 11 ppt. Siontorou et al. [253] describe electrochemical flow-injection monitoring of AFM1 in cheese samples using filter-supported bilayer lipid membrane sensors with incorporated deoxyribonucleic acid (DNA). Subnanomolar detectable toxin concentrations were reached, with a sampling rate of four samples per minute. Another group of key analytes of interest in monitoring food safety is the antimicrobial agents. These agents can be routinely administered to foodproducing animals to promote growth and for therapeutic and prophylactic reasons. This practice can lead to the introduction of such agents into the human food chain resulting in significant health risks, such as the development of Food, Beverages and Agricultural Applications 547 resistant bacterial populations and allergic responses in sensitive individuals. In addition, the milk industry can be subjected to significant losses deriving from the inhibitory effects of drug residues on the culturing/fermentation processes. As a result, regulatory authorities have stipulated maximum residue/safe tolerance levels (MRLs/STLs) in foods of animal origin [2,3] in the range of 4–200 mg kg1 for the different type of antibiotics. Analytical methods for successful routine analysis must not only meet the required limits of detections but also provide low-cost and robust alternatives to current methods. Various flow methodologies were recently developed for different antibiotics in milk samples [254–258], providing adequate analytical figures for the targeted antimicrobial agents. Other analytes measured by flow techniques in the area of milk and dairy products are the metal ions [240,243,259–271], nitrate and nitrite [224,272,273] and different carbohydrates [165,274–280]. The major effort in this area of research during the last decade has been focused on performing all the necessary sample pretreatment steps within the flow method. Table 4 summarizes some of the analytical features of these methods. 5. APPLICATIONS: MEAT AND FISH PRODUCTS Of the flow methods developed in the last years for analysis of meat and fish products, the determination of nitrate and nitrite is still one of the most common [220,221,224,295–299], probably due to the health concerns related to the formation of nitrosamine and its carcinogenity. Furthermore in the last decade, flow methods have also been developed not only for the precursors (nitrite and nitrate), but also for the nitrosamine content of food samples [300,301]. As it is pointed out in the annual reviews of atomic spectrometry, mercury continues to be the most common analyte to be determined by chemical vapour generation, and this is certainly the case in the area of food-related flow analysis. Mercury is an analyte with great importance due to its toxicity and its bioaccumulation in animal tissues. This, and the fact that chemical vapour generation is most efficiently carried out using flow systems, explains the large numbers of the articles dealing with this analyte [246,302–309]. Similar to the area of environmental geochemistry, the speciation of the different forms of mercury is gaining importance in food analysis, sometimes in the form of hyphenated FIA–HPLC systems [305]. Evaluation of freshness and quality of meat and fish products is based on sensorial evaluation. However these assays and protocols are complex and time consuming, involving a trained group of tasters and consequent elevated costs. Therefore the emerging area of development and application of the so called electronic tongue and nose – biosensor devices for recognition (identification, classification and discrimination), quantitative analysis and assessment of taste and flavour components – has been receiving increased attention. Of these components, biogenic amines are considered as useful biomarkers of food freshness. Flow methodologies have been developed for the quantification of 548 Ildikó V. Tóth et al. histamine [310–312], putrescine [313], agmatine [314] trimethylamine [315–318] content or for the assessment of a so-called freshness factor that incorporates the degenerative compounds of adenosine triphosphate (ATP) [319–321]. The developed flow procedures (Table 5) are simple in configuration and can make use of electrochemical, fluorescence, chemiluminescence detection methods, with good sensitivity, selectivity and precision, even allowing the direct introduction of the solid samples [322]. Biosensors have also been implemented in flow-injection systems with the aim to detect food pathogens such as Salmonella typhimurium and Escherichia coli. These methods are based on the separation of the target microorganism from the sample, followed by further concentration based on its highly specific reaction with immobilized antibodies. Afterwards, the detection is carried out on a piezoelectric cell [323] or amperometrically [324,325]. 6. MISCELLANEOUS FOOD PRODUCTS The application to food products that do not fall into the previous sections are presented in Table 6. These include the analysis of bread [345], butter [346], chocolate [175], edible oil [347–354], honey [42,109,132,168,278,355–360], lard [346], pasta [346,361], seasonings [362–364], soup [362,365–368], sweeteners [173, 369–377], syrup [278,378] and vinegar [77,97,109]. In general, these flow systems were devised for other matrices and are discussed in the previous sections. 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