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Molle A, Cipolat-Gotet C, Stocco G, Ferragina A, Berzaghi P, Summer A. The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese. J Dairy Sci 2024; 107:1967-1979. [PMID: 37863286 DOI: 10.3168/jds.2023-23827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/03/2023] [Indexed: 10/22/2023]
Abstract
The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.
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Affiliation(s)
- Arnaud Molle
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
| | | | - Giorgia Stocco
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
| | - Alessandro Ferragina
- Food Quality and Sensory Science Department, Teagasc Food Research Centre, D15 KN3K, Ireland
| | - Paolo Berzaghi
- University of Padova, Department of Animal Medicine, Production and Health, Padova, Italy 35020
| | - Andrea Summer
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy
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2
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Ceniti C, Spina AA, Piras C, Oppedisano F, Tilocca B, Roncada P, Britti D, Morittu VM. Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy. Foods 2023; 12:2917. [PMID: 37569186 PMCID: PMC10418805 DOI: 10.3390/foods12152917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.
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Affiliation(s)
- Carlotta Ceniti
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Anna Antonella Spina
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Cristian Piras
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Francesca Oppedisano
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Bruno Tilocca
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Paola Roncada
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Domenico Britti
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, University of Catanzaro Magna Græcia, CISVetSUA, 88100 Catanzaro, Italy
| | - Valeria Maria Morittu
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, University of Catanzaro Magna Græcia, CISVetSUA, 88100 Catanzaro, Italy
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3
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Application of UV-visible and near-infrared spectroscopies for the assessment of lysozyme addition, season of production and cow feeding in Grana Padano PDO cheese. Int Dairy J 2023. [DOI: 10.1016/j.idairyj.2023.105642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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4
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Solberg LE, Wold JP, Dankel K, Øyaas J, Måge I. In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation-A Case Study from Commercial Cheese Production. Foods 2023; 12:foods12051026. [PMID: 36900546 PMCID: PMC10001380 DOI: 10.3390/foods12051026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements.
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Affiliation(s)
- Lars Erik Solberg
- Nofima—Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291 Tromsø, Norway
- Correspondence:
| | - Jens Petter Wold
- Nofima—Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291 Tromsø, Norway
| | - Katinka Dankel
- Nofima—Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291 Tromsø, Norway
| | | | - Ingrid Måge
- Nofima—Norwegian Institute for Food, Fisheries and Aquaculture Research, Muninbakken 9-13, Breivika, 9291 Tromsø, Norway
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5
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Wójcicki K. Near-infrared spectroscopy as a green technology to monitor coffee roasting. FOODS AND RAW MATERIALS 2022. [DOI: 10.21603/2308-4057-2022-2-536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Wet chemistry methods are traditionally used to evaluate the quality of a coffee beverage and its chemical characteristics. These old methods need to be replaced with more rapid, objective, and simple analytical methods for routine analysis. Near-infrared spectroscopy is an increasingly popular technique for nondestructive quality evaluation called a green technology.
Our study aimed to apply near-infrared spectroscopy to evaluate the quality of coffee samples of different origin (Brazil, Guatemala, Peru, and Kongo). Particularly, we analyzed the roasting time and its effect on the quality of coffee. The colorimetric method determined a relation between the coffee color and the time of roasting. Partial least squares regression analysis assessed a possibility of predicting the roasting conditions from the near-infrared spectra.
The regression results confirmed the possibility of applying near-infrared spectra to estimate the roasting conditions. The correlation between the spectra and the roasting time had R2 values of 0.96 and 0.95 for calibration and validation, respectively. The root mean square errors of prediction were low – 0.92 and 1.05 for calibration and validation, respectively. We also found a linear relation between the spectra and the roasting power. The quality of the models differed depending on the coffee origin and sub-region. All the coffee samples showed a good correlation between the spectra and the brightness (L* parameter), with R2 values of 0.96 and 0.95 for the calibration and validation curves, respectively.
According to the results, near-infrared spectroscopy can be used together with the chemometric analysis as a green technology to assess the quality of coffee.
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Manzoor MF, Hussain A, Naumovski N, Ranjha MMAN, Ahmad N, Karrar E, Xu B, Ibrahim SA. A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products. Front Nutr 2022; 9:901342. [PMID: 35928834 PMCID: PMC9343702 DOI: 10.3389/fnut.2022.901342] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/31/2022] [Indexed: 01/10/2023] Open
Abstract
Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.
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Affiliation(s)
| | - Abid Hussain
- Department of Agriculture and Food Technology, Faculty of Life Science, Karakoram International University, Gilgit-Baltistan, Pakistan
| | - Nenad Naumovski
- School of Rehabilitation and Exercise Science, Faculty of Health, University of Canberra, Canberra, ACT, Australia
- Functional Foods and Nutrition Research (FFNR) Laboratory, University of Canberra, Bruce, ACT, Australia
| | | | - Nazir Ahmad
- Department of Nutritional Sciences, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Emad Karrar
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Bin Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- *Correspondence: Bin Xu
| | - Salam A. Ibrahim
- Food Microbiology and Biotechnology Laboratory, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
- Salam A. Ibrahim
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7
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Ozturk M, Dogan MA, Menevseoglu A, Ayvaz H. Infrared spectroscopy combined with chemometrics as a convenient method to detect adulterations in cooking/stretching process in commercial cheese. Int Dairy J 2022. [DOI: 10.1016/j.idairyj.2021.105312] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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8
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Hebling E Tavares JP, da Silva Medeiros ML, Barbin DF. Near-infrared techniques for fraud detection in dairy products: A review. J Food Sci 2022; 87:1943-1960. [PMID: 35362099 DOI: 10.1111/1750-3841.16143] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/09/2022] [Accepted: 03/16/2022] [Indexed: 01/14/2023]
Abstract
The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.
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Affiliation(s)
| | | | - Douglas Fernandes Barbin
- Department of Food Engineering, School of Food Engineering, University of Campinas, Campinas, Brazil
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9
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Rifna EJ, Pandiselvam R, Kothakota A, Subba Rao KV, Dwivedi M, Kumar M, Thirumdas R, Ramesh SV. Advanced process analytical tools for identification of adulterants in edible oils - A review. Food Chem 2022; 369:130898. [PMID: 34455326 DOI: 10.1016/j.foodchem.2021.130898] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/16/2021] [Accepted: 08/16/2021] [Indexed: 12/16/2022]
Abstract
This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.
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Affiliation(s)
- E J Rifna
- Department of Food Process Engineering, National Institute of Technology, Rourkela 769008, Odisha, India
| | - R Pandiselvam
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR - Central Plantation Crops Research Institute, Kasaragod 671 124, Kerala, India.
| | - Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum 695 019, Kerala, India.
| | - K V Subba Rao
- Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Madhuresh Dwivedi
- Department of Food Process Engineering, National Institute of Technology, Rourkela 769008, Odisha, India
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR-Central Institute for Research on Cotton Technology, Matunga, Mumbai 400019, India
| | - Rohit Thirumdas
- Department of Food Process Technology, College of Food Science and Technology, PJTSAU, Telangana, India
| | - S V Ramesh
- Physiology, Biochemistry and Post-Harvest Technology Division, ICAR - Central Plantation Crops Research Institute, Kasaragod 671 124, Kerala, India
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10
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Manuelian CL, Ghetti M, De Lorenzi C, Pozza M, Franzoi M, De Marchi M. Feasibility of pocket-sized near-infrared spectrometer for the prediction of cheese quality traits. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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11
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Corona P, Frangipane MT, Moscetti R, Lo Feudo G, Castellotti T, Massantini R. Chestnut Cultivar Identification through the Data Fusion of Sensory Quality and FT-NIR Spectral Data. Foods 2021; 10:2575. [PMID: 34828856 PMCID: PMC8618948 DOI: 10.3390/foods10112575] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 11/29/2022] Open
Abstract
The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other's drawbacks, synergistically contributing to an excellent result.
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Affiliation(s)
- Piermaria Corona
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; (P.C.); (R.M.); (R.M.)
- CREA Research Centre for Forestry and Wood, 52100 Arezzo, Italy
| | - Maria Teresa Frangipane
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; (P.C.); (R.M.); (R.M.)
| | - Roberto Moscetti
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; (P.C.); (R.M.); (R.M.)
| | - Gabriella Lo Feudo
- CREA Research Centre for Olive, Fruit and Citrus Crops, 87036 Rende, Italy;
| | - Tatiana Castellotti
- CREA Research Centre for Agricultural Policies and Bioeconomy, 87036 Rende, Italy;
| | - Riccardo Massantini
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; (P.C.); (R.M.); (R.M.)
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12
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Capitain C, Weller P. Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning. Molecules 2021; 26:molecules26185457. [PMID: 34576928 PMCID: PMC8468721 DOI: 10.3390/molecules26185457] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 12/20/2022] Open
Abstract
Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.
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13
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Boughattas F, Karoui R. Mid infrared spectroscopy combined with chemometric tools for the identification of canned tuna species in brine. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2020.103717] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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14
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Anyidoho EK, Teye E, Agbemafle R. Nondestructive authentication of the regional and geographical origin of cocoa beans by using a handheld NIR spectrometer and multivariate algorithm. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:4150-4158. [PMID: 32776043 DOI: 10.1039/d0ay00901f] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Traceability in the cocoa bean trade is vital to ensuring quality. In this study, a handheld near-infrared (NIR) spectrometer was attempted for rapid and nondestructive regional and geographical classification of cocoa beans from different locations. Cocoa bean samples collected from seven cocoa-producing regions in Ghana (Eastern, Ashanti, Volta, Western South, Western North, Central, and Brong Ahafo) and four cocoa-producing countries in Africa (Uganda, Ivory Coast, Nigeria, and Ghana) were used. Among the preprocessing techniques employed, multiplicative scatter correction (MSC) performed better. The correct classification rate for the seven cocoa-producing regions in Ghana was 100% for LDA and SVM models in the training set and testing set. For classification of cocoa beans based on the country of origin, LDA and SVM also gave 100% classification rate both in the training set and testing set. The results give strong indications that hand-held spectroscopy coupled with chemometrics could be employed to provide the quick, accurate, and nondestructive classification of cocoa beans according to different locations. This technique could improve the work of quality control inspectors both from industry and regulatory perspectives for effective and quick detection of cocoa bean fraud.
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Affiliation(s)
- Elliot K Anyidoho
- University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana
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15
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Ferone M, Gowen A, Fanning S, Scannell AGM. Microbial detection and identification methods: Bench top assays to omics approaches. Compr Rev Food Sci Food Saf 2020; 19:3106-3129. [PMID: 33337061 DOI: 10.1111/1541-4337.12618] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/20/2020] [Accepted: 07/20/2020] [Indexed: 12/26/2022]
Abstract
Rapid detection of foodborne pathogens, spoilage microbes, and other biological contaminants in complex food matrices is essential to maintain food quality and ensure consumer safety. Traditional methods involve culturing microbes using a range of nonselective and selective enrichment methods, followed by biochemical confirmation among others. The time-to-detection is a key limitation when testing foods, particularly those with short shelf lives, such as fresh meat, fish, dairy products, and vegetables. Some recent detection methods developed include the use of spectroscopic techniques, such as matrix-assisted laser desorption ionization-time of flight along with hyperspectral imaging protocols.This review presents a comprehensive overview comparing insights into the principles, characteristics, and applications of newer and emerging techniques methods applied to the detection and identification of microbes in food matrices, to more traditional benchtop approaches. The content has been developed to provide specialist scientists a broad view of bacterial identification methods available in terms of their benefits and limitations, which may be useful in the development of future experimental design. The case is also made for incorporating some of these emerging methods into the mainstream, for example, underutilized potential of spectroscopic techniques and hyperspectral imaging.
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Affiliation(s)
- Mariateresa Ferone
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD School of Biosystems and Food Engineering, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland
| | - Aoife Gowen
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD School of Biosystems and Food Engineering, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland
| | - Séamus Fanning
- UCD Institute of Food and Health, Dublin, Ireland.,UCD-Centre for Food Safety, Dublin, Ireland.,UCD School of Public Health, Physiotherapy and Sport Science University College Dublin, Dublin, Ireland
| | - Amalia G M Scannell
- UCD School of Agriculture and Food Science, Dublin, Ireland.,UCD Institute of Food and Health, Dublin, Ireland.,UCD-Centre for Food Safety, Dublin, Ireland
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16
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Abstract
AbstractIn cheese-like products, milk components (in particular fat) are partially or completely replaced with non-dairy substitutes. An attempt was made in this study to determine whether Edam-type cheese can be distinguished from its substitute, where milk fat was replaced with palm oil, based on rheological properties. The rheological properties of Edam cheese and its substitute were analyzed during a 16-week ripening period, based on the results of a stress-relaxation test. The values of the rheological parameters were estimated with the use of the generalized Maxwell model and a non-linear model proposed by the authors, which accounted for the plastic deformation of the analyzed samples. The study revealed that both methods were equally effective in describing the stress relaxation process; therefore, they can be regarded as equivalent. Excluding the initial stage of ripening (which is not important from the consumers’ point of view), the replacement of milk fat with palm oil did not influence the rheological properties of Edam-type cheese and the cheese-like product. In subsequent stages of ripening, no significant differences were found in the rheological properties of both products, which could only be used to evaluate their ripeness.
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17
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Mid infrared spectroscopy coupled with chemometric tools for qualitative analysis of canned tuna with sunflower medium. J Food Compost Anal 2020. [DOI: 10.1016/j.jfca.2020.103519] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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18
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Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107111] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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19
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Li X, Tsuta M, Tanaka F, Tsukahara M, Tsukahara K. Assessment of Japanese Awamori Spirits Using UV–VIS Spectroscopy. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01692-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Cadet XF, Lo-Thong O, Bureau S, Dehak R, Bessafi M. Use of Machine Learning and Infrared Spectra for Rheological Characterization and Application to the Apricot. Sci Rep 2019; 9:19197. [PMID: 31844151 PMCID: PMC6915699 DOI: 10.1038/s41598-019-55543-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 11/29/2019] [Indexed: 12/04/2022] Open
Abstract
Fast advancement of machine learning methods and constant growth of the areas of application open up new horizons for large data management and processing. Among the various types of data available for analysis, the Fourier Transform InfraRed (FTIR) spectroscopy spectra are very challenging datasets to consider. In this study, machine learning is used to analyze and predict a rheological parameter: firmness. Various statistics have been gathered including both chemistry (such as ethylene, titrable acidity or sugars) and spectra values to visualize and analyze a dataset of 731 biological samples. Two-dimensional (2D) and three-dimensional (3D) principal component analyses (PCA) are used to evaluate their ability to discriminate for one parameter: firmness. Partial least squared regression (PLSR) modeling has been carried out to predict the rheological parameter using either sixteen physicochemical parameters or only the infrared spectra. We show that (i) the spectra alone allows good discrimination of the samples based on rheology, (ii) 3D-PCA allows comprehensive and informative visualization of the data, and (iii) that the rheological parameters are predicted accurately using a regression method such as PLSR; instead of using chemical parameters which are laborious to obtain, Mid-FTIR spectra gathering all physicochemical information could be used for efficient prediction of firmness. As a conclusion, rheological and chemical parameters allow good discrimination of the samples according to their firmness. However, using only the IR spectra leads to better results. A good predictive model was built for the prediction of the firmness of the fruit, and we reached a coefficient of determination R2 value of 0.90. This method outperforms a model based on physicochemical descriptors only. Such an approach could be very helpful to technologists and farmers.
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Affiliation(s)
- Xavier F Cadet
- PEACCEL, Protein Engineering Accelerator, 6 square Albin Cachot, box 42, 75013, Paris, France. .,LSE laboratory, EPITA, Paris, 94276, France.
| | - Ophélie Lo-Thong
- University of Paris, UMR_S1134, BIGR, Inserm, F-75015, Paris, France.,DSIMB, UMR_S1134, BIGR, Inserm, Laboratory of Excellence GR-Ex, Faculty of Sciences and Technology, University of La Reunion, F-97715, Saint-Denis, France
| | - Sylvie Bureau
- UMR408 SQPOV, Sécurité et Qualité des Produits d'Origine Végétale, INRA, Avignon University, F-84000, Avignon, France
| | - Reda Dehak
- LSE laboratory, EPITA, Paris, 94276, France
| | - Miloud Bessafi
- LE2P, Laboratory of Energy, Electronics and Processes EA 4079, Faculty of Sciences and Technology, University of La Reunion, 97444, St Denis Cedex, France
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Stocco G, Cipolat-Gotet C, Ferragina A, Berzaghi P, Bittante G. Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and spectrum intervals. J Dairy Sci 2019; 102:9622-9638. [PMID: 31477307 DOI: 10.3168/jds.2019-16770] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 07/06/2019] [Indexed: 11/19/2022]
Abstract
Near-infrared spectroscopy (NIRS) has been widely used to determine various composition traits of many dairy products in the industry. In the last few years, near-infrared (NIR) instruments have become more and more accessible, and now, portable devices can be easily used in the field, allowing the direct measurement of important quality traits. However, the comparison of the predictive performances of different NIR instruments is not simple, and the literature is lacking. These instruments may use different wavelength intervals and calibration procedures, making it difficult to establish whether differences are due to the spectral interval, the chemometric approach, or the instrument's technology. Hence, the aims of this study were (1) to evaluate the prediction accuracy of chemical contents (5 traits), pH, texture (2 traits), and color (5 traits) of 37 categories of cheese; (2) to compare 3 instruments [2 benchtop, working in reflectance (R) and transmittance (T) mode (NIRS-R and NIRS-T, respectively) and 1 portable device (VisNIRS-R)], using their entire spectral ranges (1100-2498, 850-1048, and 350-1830 nm, respectively, for NIRS-R, NIRS-T and VisNIRS-R); (3) to examine different wavelength intervals of the spectrum within instrument, comparing also the common intervals among the 3 instruments; and (4) to determine the presence of bias in predicted traits for specific cheese categories. A Bayesian approach was used to develop 8 calibration models for each of 13 traits. This study confirmed that NIR spectroscopy can be used to predict the chemical composition of a large number of different cheeses, whereas pH and texture traits were poorly predicted. Color showed variable predictability, according to the trait considered, the instrument used, and, within instrument, according to the wavelength intervals. The predictive performance of the VisNIRS-R portable device was generally better than the 2 laboratory NIRS instruments, whether with the entire spectrum or selected intervals. The VisNIRS-R was found suitable for analyzing chemical composition in real time, without the need for sample uptake and processing. Our results also indicated that instrument technology is much more important than the NIR spectral range for accurate prediction equations, but the visible range is useful when predicting color traits, other than lightness. Specifically for certain categories (i.e., caprine, moldy, and fresh cheeses), dedicated calibrations seem to be needed to obtain unbiased and more accurate results.
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Affiliation(s)
- Giorgia Stocco
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy; Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy.
| | - Claudio Cipolat-Gotet
- Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy
| | - Alessandro Ferragina
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Paolo Berzaghi
- Department of Animal Medicine, Production and Health (MAPS), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
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22
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Ma YB, Babu KS, Amamcharla JK. Prediction of total protein and intact casein in cheddar cheese using a low-cost handheld short-wave near-infrared spectrometer. Lebensm Wiss Technol 2019. [DOI: 10.1016/j.lwt.2019.04.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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23
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Khattab AR, Guirguis HA, Tawfik SM, Farag MA. Cheese ripening: A review on modern technologies towards flavor enhancement, process acceleration and improved quality assessment. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.03.009] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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24
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Developments of nondestructive techniques for evaluating quality attributes of cheeses: A review. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.04.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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25
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Strani L, Grassi S, Casiraghi E, Alamprese C, Marini F. Milk Renneting: Study of Process Factor Influences by FT-NIR Spectroscopy and Chemometrics. FOOD BIOPROCESS TECH 2019. [DOI: 10.1007/s11947-019-02266-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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26
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Okubo N, Kurata Y. Nondestructive Classification Analysis of Green Coffee Beans by Using Near-Infrared Spectroscopy. Foods 2019; 8:E82. [PMID: 30813296 PMCID: PMC6406992 DOI: 10.3390/foods8020082] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/12/2019] [Accepted: 02/18/2019] [Indexed: 11/16/2022] Open
Abstract
Near-infrared spectroscopy (NIRS) is a powerful tool for the nondestructive evaluation of organic materials, and it has found widespread use in a variety of industries. In the food industry, it is important to know the district in which a particular food was produced. Therefore, in this study, we focused on determining the production area (five areas and three districts) of green coffee beans using classification analysis and NIRS. Soft independent modeling of class analogy (SIMCA) was applied as the classification method. Samples of green coffee beans produced in seven locations-Cuba, Ethiopia, Indonesia (Bari, Java, and Sumatra), Tanzania, and Yemen-were analyzed. These regions were selected since green coffee beans from these locations are commonly sold in Japan supermarkets. A good classification result was obtained with SIMCA for the seven green bean samples, although some samples were partly classified into several categories. Then, the model distance values of SIMCA were calculated and compared. A few model distance values were ~10; such small values may be the reason for misclassification. However, over a 73% correct classification rate could be achieved for the different kinds of green coffee beans using NIRS.
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Affiliation(s)
- Naoya Okubo
- Graduate School of Bioresource Sciences, Nihon University, 1866 Kameino, Fujisawa, Kanagawa 252-0880, Japan.
| | - Yohei Kurata
- College of Bioresource Sciences, Nihon University, 1866 Kameino, Fujisawa, Kanagawa 252-0880, Japan.
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Wiedemair V, Langore D, Garsleitner R, Dillinger K, Huck C. Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis. Molecules 2019; 24:E428. [PMID: 30682872 PMCID: PMC6385083 DOI: 10.3390/molecules24030428] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 01/18/2019] [Accepted: 01/22/2019] [Indexed: 11/16/2022] Open
Abstract
The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R²) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.
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Affiliation(s)
- Verena Wiedemair
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria.
| | - Dominik Langore
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria.
| | - Roman Garsleitner
- Chemical devision, HBLFA für Landwirtschaft und Ernährung, Lebensmittel und Biotechnologie Tirol,Rotholz 50a, 6200 Strass im Zillertal, Austria.
| | - Klaus Dillinger
- Chemical devision, HBLFA für Landwirtschaft und Ernährung, Lebensmittel und Biotechnologie Tirol,Rotholz 50a, 6200 Strass im Zillertal, Austria.
| | - Christian Huck
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria.
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28
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Nondestructive Near-Infrared Spectroscopic Analysis of Oils on Wood Surfaces. FORESTS 2019. [DOI: 10.3390/f10010064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The further use of wood resources is expected in an environmentally conscious society. Added-value, such as durability enhancement and preservation by painting, are needed to expand the applicability of wood. Assessment of wood properties such as surface and coat adhesion can be made by studying perviousness to liquid oils, with the aim of developing wood products that deter insects and are weather-resistant; hence, discriminant analysis of oil type is important. Near-infrared (NIR) spectroscopy is a powerful tool for nondestructive characterization of organic materials and has been widely used in many industries. Here, NIR detection of oil on wood surfaces is applied for the distinguishing of three different types of oil (hereafter, “Oil_1”, “Oil_2” and “Oil_3”) via soft independent modeling of class analogy (SIMCA). Oil_1 was antiseptic vehicle or cutting oil. Oil_2 was used as a motor oil for an oil pressure machine. Oil_3 was plant-derived oil. Two types of wood that are commonly used in Japanese construction (Cryptomeria japonica and Chamaecyparis obtuse) were analyzed after applying oil. The NIR spectra measured after the oil was applied were greater in the ranges 1700–1800 nm and 2300–2500 nm than spectra for the bare wood sample. As SIMCA analyses were performed by using spectral data that included the moving average, baseline correction and second derivatives, good results were obtained for Oil_3 for both wood samples. However, the correct classification percentages were low for Oil_1, and the percentage of samples classified within several categories was high. If the components are very different, such as those for Oil_3, NIRS can be a powerful non-destructive method for identifying oil in the context of wood products testing.
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29
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Su WH, Sun DW. Advanced Analysis of Roots and Tubers by Hyperspectral Techniques. ADVANCES IN FOOD AND NUTRITION RESEARCH 2018; 87:255-303. [PMID: 30678816 DOI: 10.1016/bs.afnr.2018.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.
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Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland.
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30
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Holden NM, White EP, Lange MC, Oldfield TL. Review of the sustainability of food systems and transition using the Internet of Food. NPJ Sci Food 2018; 2:18. [PMID: 31304268 PMCID: PMC6550226 DOI: 10.1038/s41538-018-0027-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 09/10/2018] [Accepted: 09/12/2018] [Indexed: 11/12/2022] Open
Abstract
Many current food systems are unsustainable because they cause significant resource depletion and unacceptable environmental impacts. This problem is so severe, it can be argued that the food eaten today is equivalent to a fossil resource. The transition to sustainable food systems will require many changes but of particular importance will be the harnessing of internet technology, in the form of an ‘Internet of Food’, which offers the chance to use global resources more efficiently, to stimulate rural livelihoods, to develop systems for resilience and to facilitate responsible governance by means of computation, communication, education and trade without limits of knowledge and access. A brief analysis of the evidence of resource depletion and environmental impact associated with food production and an outline of the limitations of tools like life cycle assessment, which are used to quantify the impact of food products, indicates that the ability to combine data across the whole system from farm to human will be required in order to design sustainable food systems. Developing an Internet of Food, as a precompetitive platform on which business models can be built, much like the internet as we currently know it, will require agreed vocabularies and ontologies to be able to reason and compute across the vast amounts of data that are becoming available. The ability to compute over large amounts of data will change the way the food system is analysed and understood and will permit a transition to sustainable food systems.
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Affiliation(s)
- Nicholas M Holden
- 1UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin, 4 Ireland
| | | | - Matthew C Lange
- 3Department of Food Science and Technology, University of California, Davis, CA 95616 USA
| | - Thomas L Oldfield
- 1UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin, 4 Ireland
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31
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Quantification and visualization of α-tocopherol in oil-in-water emulsion based delivery systems by Raman microspectroscopy. Lebensm Wiss Technol 2018. [DOI: 10.1016/j.lwt.2018.05.017] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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32
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Pasquini C. Near infrared spectroscopy: A mature analytical technique with new perspectives – A review. Anal Chim Acta 2018; 1026:8-36. [DOI: 10.1016/j.aca.2018.04.004] [Citation(s) in RCA: 363] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 04/05/2018] [Accepted: 04/06/2018] [Indexed: 12/19/2022]
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33
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Henihan LE, O’Donnell CP, Esquerre C, Murphy EG, O’Callaghan DJ. Quality Assurance of Model Infant Milk Formula Using a Front-Face Fluorescence Process Analytical Tool. FOOD BIOPROCESS TECH 2018. [DOI: 10.1007/s11947-018-2112-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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34
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Panikuttira B, O'Shea N, Tobin JT, Tiwari BK, O'Donnell CP. Process analytical technology for cheese manufacture. Int J Food Sci Technol 2018. [DOI: 10.1111/ijfs.13806] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Bhavya Panikuttira
- School of Biosystems and Food Engineering; University College Dublin; Belfield D4 Dublin Ireland
| | - Norah O'Shea
- Food Chemistry and Technology Department; Teagasc Food Research Centre; Moorepark, Fermoy Co.Cork Ireland
| | - John T. Tobin
- Food Chemistry and Technology Department; Teagasc Food Research Centre; Moorepark, Fermoy Co.Cork Ireland
| | - Brijesh K. Tiwari
- Food Chemistry and Technology Department; Teagasc Food Research Centre; Ashtown D15 Dublin Ireland
| | - Colm P. O'Donnell
- School of Biosystems and Food Engineering; University College Dublin; Belfield D4 Dublin Ireland
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35
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36
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Su WH, Sun DW. Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review. Compr Rev Food Sci Food Saf 2017; 17:104-122. [DOI: 10.1111/1541-4337.12314] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 09/12/2017] [Accepted: 09/14/2017] [Indexed: 12/13/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
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37
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Currò S, Manuelian C, Penasa M, Cassandro M, De Marchi M. Technical note: Feasibility of near infrared transmittance spectroscopy to predict cheese ripeness. J Dairy Sci 2017; 100:8759-8763. [DOI: 10.3168/jds.2017-13001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2017] [Accepted: 08/02/2017] [Indexed: 11/19/2022]
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38
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Calibration Transfer from Micro NIR Spectrometer to Hyperspectral Imaging: a Case Study on Predicting Soluble Solids Content of Bananito Fruit (Musa acuminata). FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1055-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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39
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Manuelian CL, Currò S, Penasa M, Cassandro M, De Marchi M. Prediction of minerals, fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy. Int Dairy J 2017. [DOI: 10.1016/j.idairyj.2017.03.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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40
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Manuelian C, Currò S, Visentin G, Penasa M, Cassandro M, Dellea C, Bernardi M, De Marchi M. Technical note: At-line prediction of mineral composition of fresh cheeses using near-infrared technologies. J Dairy Sci 2017. [DOI: 10.3168/jds.2017-12634] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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41
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Qu JH, Liu D, Cheng JH, Sun DW, Ma J, Pu H, Zeng XA. Applications of near-infrared spectroscopy in food safety evaluation and control: a review of recent research advances. Crit Rev Food Sci Nutr 2016; 55:1939-54. [PMID: 24689758 DOI: 10.1080/10408398.2013.871693] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Food safety is a critical public concern, and has drawn great attention in society. Consequently, developments of rapid, robust, and accurate methods and techniques for food safety evaluation and control are required. As a nondestructive and convenient tool, near-infrared spectroscopy (NIRS) has been widely shown to be a promising technique for food safety inspection and control due to its huge advantages of speed, noninvasive measurement, ease of use, and minimal sample preparation requirement. This review presents the fundamentals of NIRS and focuses on recent advances in its applications, during the last 10 years of food safety control, in meat, fish and fishery products, edible oils, milk and dairy products, grains and grain products, fruits and vegetables, and others. Based upon these applications, it can be demonstrated that NIRS, combined with chemometric methods, is a powerful tool for food safety surveillance and for the elimination of the occurrence of food safety problems. Some disadvantages that need to be solved or investigated with regard to the further development of NIRS are also discussed.
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Affiliation(s)
- Jia-Huan Qu
- a College of Light Industry and Food Sciences, South China University of Technology , Guangzhou , PR China
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43
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Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat. Food Chem 2015; 175:417-22. [DOI: 10.1016/j.foodchem.2014.11.161] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2014] [Revised: 11/28/2014] [Accepted: 11/29/2014] [Indexed: 12/22/2022]
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44
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Towards improvement in classification of Escherichia coli, Listeria innocua and their strains in isolated systems based on chemometric analysis of visible and near-infrared spectroscopic data. J FOOD ENG 2015. [DOI: 10.1016/j.jfoodeng.2014.09.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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