1
|
Su Y, Zhang J, Wang L, Jin G, Zhang A. Signature of Sr isotope ratios and the contents of elements as a tool to distinguish wine regions in China. Food Chem 2024; 446:138812. [PMID: 38408400 DOI: 10.1016/j.foodchem.2024.138812] [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: 11/14/2023] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 02/28/2024]
Abstract
This study investigated 120 Chinese wines from seven regions and had two objectives: to clarify the Sr isotope ratios and elemental characteristics of each region and to develop a strategy to distinguish the geographic origin of wine without authentic samples to predict its origin. The analyzed 87Sr/86Sr values ranged from 0.708256 to 0.715148, which correlated with the geological characteristics of the regions where they were grown. The Hexi Corridor exhibited the highest ratios of Sr isotopes, while Xinjiang had the lowest. The 87Sr/86Sr values were applied to establish a prediction map which was evaluated through cross-validation. The prediction error was found to be less than 0.00074. The Sr isotope ratio could remain stable for an extended period in a specific location. This map shows the feasibility of identifying wine origin and could be applied to other food products. Adding Sr isotope ratios could improve the accuracy in tracing wine origin.
Collapse
Affiliation(s)
- Yingyue Su
- Technology Center of Qinhuangdao Customs, Qinhuangdao 066004, PR China; Northwest A&F University, Yangling 712100, PR China; Hebei Key Laboratory of Wine Quality & Safety Testing, Qinhuangdao 066004, PR China
| | - Jiancai Zhang
- Hebei Normal University of Science and Technology, Qinhuangdao 066004, PR China
| | - Lishan Wang
- Technology Center of Qinhuangdao Customs, Qinhuangdao 066004, PR China; Hebei Key Laboratory of Wine Quality & Safety Testing, Qinhuangdao 066004, PR China
| | - Gang Jin
- Ningxia University, Yinchuan 750021, PR China.
| | - Ang Zhang
- Technology Center of Qinhuangdao Customs, Qinhuangdao 066004, PR China; Hebei Key Laboratory of Wine Quality & Safety Testing, Qinhuangdao 066004, PR China.
| |
Collapse
|
2
|
Wang H, Jeffery DW. Machine Learning Model Stability for Sub-Regional Classification of Barossa Valley Shiraz Wine Using A-TEEM Spectroscopy. Foods 2024; 13:1376. [PMID: 38731746 PMCID: PMC11083604 DOI: 10.3390/foods13091376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
With a view to maintaining the reputation of wine-producing regions among consumers, minimising economic losses caused by wine fraud, and achieving the purpose of data-driven terroir classification, the use of an absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) technique has shown great potential based on the molecular fingerprinting of a sample. The effects of changes in wine composition due to ageing and the stability of A-TEEM models over time had not been addressed, however, and the classification of wine blends required investigation. Thus, A-TEEM data were combined with an extreme gradient boosting discriminant analysis (XGBDA) algorithm to build classification models based on a range of Shiraz research wines (n = 217) from five Barossa Valley sub-regions over four vintages that had aged in bottle for several years. This spectral fingerprinting and machine learning approach revealed a 100% class prediction accuracy based on cross-validation (CV) model results for vintage year and 98.8% for unknown sample prediction accuracy when splitting the wine samples into training and test sets to obtain the classification models. The modelling and prediction of sub-regional production area showed a class CV prediction accuracy of 99.5% and an unknown sample prediction accuracy of 93.8% when modelling with the split dataset. Inputting a sub-set of the current A-TEEM data into the models generated previously for these Barossa sub-region wines yielded a 100% accurate prediction of vintage year for 2018-2020 wines, 92% accuracy for sub-region for 2018 wines, and 91% accuracy for sub-region using 2021 wine spectral data that were not included in the original modelling. Satisfactory results were also obtained from the modelling and prediction of blended samples for the vintages and sub-regions, which is of significance when considering the practice of wine blending.
Collapse
Affiliation(s)
| | - David W. Jeffery
- School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
| |
Collapse
|
3
|
Zhou X, Li L, Zheng J, Wu J, Wen L, Huang M, Ao F, Luo W, Li M, Wang H, Zong X. Quantitative analysis of key components in Qingke beer brewing process by multispectral analysis combined with chemometrics. Food Chem 2024; 436:137739. [PMID: 37839128 DOI: 10.1016/j.foodchem.2023.137739] [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: 07/10/2023] [Revised: 09/16/2023] [Accepted: 10/10/2023] [Indexed: 10/17/2023]
Abstract
In order to monitor the Qingke beer brewing process in real time, this paper presents an analytical method for predicting the content of key components in the wort during the mashing and boiling stages using multi-spectroscopy combined with chemometrics. The results showed that the Neural Networks (NN) model based on Raman spectroscopy (RPD = 3.9727) and the NN model based on NIR spectroscopy (RPD = 5.1952) had the best prediction performance for the reducing sugar content in the mashing and boiling stages; The partial least Squares (PLS) model based on Raman spectroscopy (RPD = 2.7301) and the NN model based on Raman spectroscopy (RPD = 4.3892) predicted the content of free amino nitrogen best; The PLS model based on UV-Vis spectroscopy (RPD = 4.0412) and the NN model based on Raman spectroscopy (RPD = 4.0540) are most suitable for the quantitative analysis of total phenols. The results can be used as a guide for real-time control of wort quality in industrial production.
Collapse
Affiliation(s)
- Xianjiang Zhou
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Li Li
- College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Jia Zheng
- Wuliangye Group Co., Ltd, Yibin 644000, Sichuan, China.
| | - Jianhang Wu
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Lei Wen
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Min Huang
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Feng Ao
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Wenli Luo
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| | - Mao Li
- Wuliangye Group Co., Ltd, Yibin 644000, Sichuan, China.
| | - Hong Wang
- Wuliangye Group Co., Ltd, Yibin 644000, Sichuan, China.
| | - Xuyan Zong
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China; College of Bioengineering, Sichuan University of Science and Engineering, Yibin 644000, Sichuan, China.
| |
Collapse
|
4
|
Carrasco-Quiroz M, Martínez-Gil AM, Nevares I, del Alamo-Sanza M. New System for Simultaneous Measurement of Oxygen Consumption and Changes in Wine Color. Molecules 2023; 29:231. [PMID: 38202815 PMCID: PMC10780306 DOI: 10.3390/molecules29010231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 12/22/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
The design, construction and validation of a device for the accurate measurement of the dissolved oxygen content in wine and simultaneously the variation of its spectral fingerprint is presented. The novelty of this system is due to two innovative approaches. First, robustness in measurements is obtained by using cuvettes designed to simultaneously measure the dissolved oxygen and color. Secondly, automatic monitoring is performed to ensure that measurements are always taken at the same cuvette position. The fine-tuning of the device with the study of white and red wines makes it possible, on the one hand, to establish the appropriate measurement conditions and, on the other hand, to determine the amount of oxygen required to cause specific changes in the wine spectrum, information that could not be obtained until now. The preliminary results are very interesting, presenting precise data on the amount of oxygen consumed by the wine and the variations in its visible spectrum, thus reflecting the modification of the responsible phenolic compounds. This information is of great interest, since it helps to optimize the handling of the wine and, if necessary, to moderate the uptake of oxygen in each type of wine to ensure the maintenance of the color during the winemaking and conservation processes of each type of wine. The results of the experiments indicate that this new instrument is feasible and accurate for detecting oxygen changes during wine production.
Collapse
Affiliation(s)
- Marioli Carrasco-Quiroz
- Department of Analytical Chemistry, UVaMOX—Universidad de Valladolid, 34004 Palencia, Spain; (M.C.-Q.); (A.M.M.-G.)
| | - Ana María Martínez-Gil
- Department of Analytical Chemistry, UVaMOX—Universidad de Valladolid, 34004 Palencia, Spain; (M.C.-Q.); (A.M.M.-G.)
| | - Ignacio Nevares
- Department of Agroforestry Engineering, UVaMOX—Universidad de Valladolid, 34004 Palencia, Spain
| | - Maria del Alamo-Sanza
- Department of Analytical Chemistry, UVaMOX—Universidad de Valladolid, 34004 Palencia, Spain; (M.C.-Q.); (A.M.M.-G.)
| |
Collapse
|
5
|
Kim YM, Lubinska-Szczygeł M, Park YS, Deutsch J, Ezra A, Luksrikul P, Beema Shafreen RM, Gorinstein S. Characterization of Bioactivity of Selective Molecules in Fruit Wines by FTIR and NMR Spectroscopies, Fluorescence and Docking Calculations. Molecules 2023; 28:6036. [PMID: 37630288 PMCID: PMC10457986 DOI: 10.3390/molecules28166036] [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: 06/10/2023] [Revised: 07/30/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023] Open
Abstract
Fourier transform infrared (FTIR) and proton nuclear magnetic resonance (1H NMR) spectroscopies were applied to characterize and compare the chemical shifts in the polyphenols' regions of some fruit wines. The obtained results showed that FTIR spectra (1800-900 cm-1) and 1H NMR (δ 6.5-9.3 ppm) of different fruit wines can be used as main indices of the year of vintage and quality of fruit wines. In addition to the classical determination of antioxidant profiles and bioactive substances in wines, fluorometric measurements were used to determine the interactions of wine substances with the main human serum proteins. The results showed relatively high binding properties of wines with the highest one for pomegranate, followed by kiwifruit and persimmon wines. The interactions of vitamin C, catechin and gallic acid with human serum albumin (HSA) were also examined by docking studies. The docking calculations showed that gallic acid has a stronger binding affinity compared to catechin and vitamin C. The stronger binding affinity of gallic acid may be due to three hydrogen bonds and pi-pi interactions. The fluorescence and docking studies proved that only the bioactive compounds of wines and not the amount of alcohol have high binding properties to human serum proteins. The emphasis in this report was made on the utility of FTIR, NMR and fluorescence of wines as a mean of wine authentication and its fingerprint. The findings, based on polyphenols from fruits and fruit wines, their bioactivity and health properties, offer valuable insights for future endeavours focused on designing healthy food products.
Collapse
Affiliation(s)
- Young-Mo Kim
- Industry Academic Collaboration Foundation, Kwangju Women’s University, Gwangju 62396, Republic of Korea;
| | - Martyna Lubinska-Szczygeł
- Department of Analytical Chemistry, Faculty of Chemistry, Gdansk University of Technology, 80-233 Gdansk, Poland
| | - Yong-Seo Park
- Department of Horticultural Science, Mokpo National University, Muan 58554, Republic of Korea;
| | - Joseph Deutsch
- Faculty of Medicine, Institute for Drug Research, School of Pharmacy, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel; (J.D.); (A.E.)
| | - Aviva Ezra
- Faculty of Medicine, Institute for Drug Research, School of Pharmacy, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel; (J.D.); (A.E.)
| | - Patraporn Luksrikul
- Department of Chemistry, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand;
- Center for Advanced Studies in Nanotechnology for Chemical, Food and Agricultural Industries, KU Institute for Advanced Studies, Kasetsart University, Bangkok 10900, Thailand
| | - Raja Mohamed Beema Shafreen
- Dr Umayal Ramanathan College for Women, Alagappa University, Alagappapuram, Karaikudi 630003, Tamilnadu, India
| | - Shela Gorinstein
- Faculty of Medicine, Institute for Drug Research, School of Pharmacy, The Hebrew University of Jerusalem, Jerusalem 9112001, Israel; (J.D.); (A.E.)
| |
Collapse
|
6
|
Gerginova D, Simova S. Chemical Profiling of Wines Produced in Bulgaria and Distinction from International Grape Varieties. ACS OMEGA 2023; 8:18702-18713. [PMID: 37273597 PMCID: PMC10233681 DOI: 10.1021/acsomega.3c00636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/04/2023] [Indexed: 06/06/2023]
Abstract
Distinguishing the botanical and geographical origin of wine is important to prevent wine adulteration and to determine its quality. The combined use of 1H NMR profiling and chemometrics allows the quantification of 31 common organic components in the NMR spectra of 70 wines from different sources. Using the NMR metabolomics approach, a successful differentiation of wines produced from Bulgarian and international grape varieties is achieved using linear discriminant analysis. Wines produced from typical local grape varieties contain higher average amounts of galacturonic, malic, tartaric, and succinic acid, alanine, choline, several alcohols, and saccharides arabinose, galactose, and sucrose than imported wine assortments. A practical decision tree is proposed for distinguishing 15 different grape varieties based on the amounts of the common wine components. An example of distinction of real from diluted wine via creation of a PLS-DA model is presented. Wines from the two subregions officially recognized by the EU at the Protected Geographical Indication (PGI) level are unequivocally recognized.
Collapse
|
7
|
Temerdashev Z, Bolshov M, Abakumov A, Khalafyan A, Kaunova A, Vasilyev A, Sheludko O, Ramazanov A. Can Rare Earth Elements Be Considered as Markers of the Varietal and Geographical Origin of Wines? Molecules 2023; 28:molecules28114319. [PMID: 37298795 DOI: 10.3390/molecules28114319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/11/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
The possibility of establishing the varietal and territorial affiliation of wines by the content of rare earth elements (REE) in them was studied. ICP-OES and ICP-MS with subsequent chemometric processing of the results were applied to determine the elemental image of soils containing negligible REE amounts, grapes grown on these soils, and wine materials of Cabernet Sauvignon, Merlot, and Moldova varieties produced from these grapes. To stabilize and clarify wine materials, the traditional processing of wine materials with various types of bentonite clays (BT) was used, which turned out to be a source of REE in the wine material. Discriminant analysis revealed that the processed wine materials were homogeneous within one denomination and that those of different denominations were heterogeneous with respect to the content of REE. It was found that REE in wine materials were transferred from BT during the processing, and thus they can poorly characterize the geographical origin and varietal affiliation of wines. Analysis of these wine materials according to the intrinsic concentrations of macro- and microelements showed that they formed clusters according to their varietal affiliation. In terms of their influence on the varietal image of wine materials, REE are significantly inferior to macro- and microelements, but they enhance their influence to a certain extent when used together.
Collapse
Affiliation(s)
- Zaual Temerdashev
- Analytical Chemistry Department, Faculty of Chemistry and High Technologies, Kuban State University, Krasnodar 350040, Russia
| | - Mikhail Bolshov
- Institute for Spectroscopy, Russian Academy of Sciences, Troitsk, Moscow 108840, Russia
| | - Aleksey Abakumov
- Analytical Chemistry Department, Faculty of Chemistry and High Technologies, Kuban State University, Krasnodar 350040, Russia
| | - Alexan Khalafyan
- Analytical Chemistry Department, Faculty of Chemistry and High Technologies, Kuban State University, Krasnodar 350040, Russia
| | - Anastasia Kaunova
- Analytical Chemistry Department, Faculty of Chemistry and High Technologies, Kuban State University, Krasnodar 350040, Russia
| | - Alexander Vasilyev
- Analytical Chemistry Department, Faculty of Chemistry and High Technologies, Kuban State University, Krasnodar 350040, Russia
| | - Olga Sheludko
- North Caucasian Federal Research Center of Horticulture, Viticulture, Wine-Making, Krasnodar 350072, Russia
| | - Arsen Ramazanov
- Institute for Geothermal Problems and Renewable Energy, Branch of the Joint Institute of High Temperatures of the Russian Academy of Sciences, Makhachkala 367030, Russia
| |
Collapse
|
8
|
Nicoleti JL, Braga ES, Stanisic D, Jadranin M, Façanha DAE, Barral TD, Hanna SA, Azevedo V, Meyer R, Tasic L, Portela RW. A serum NMR metabolomic analysis of the Corynebacterium pseudotuberculosis infection in goats. Appl Microbiol Biotechnol 2023:10.1007/s00253-023-12595-0. [PMID: 37219572 DOI: 10.1007/s00253-023-12595-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 05/24/2023]
Abstract
Caseous lymphadenitis (CLA), an infectious disease caused by Corynebacterium pseudotuberculosis in small ruminants, is highly prevalent worldwide. Economic losses have already been associated with the disease, and little is known about the host-pathogen relationship associated with the disease. The present study aimed to perform a metabolomic study of the C. pseudotuberculosis infection in goats. Serum samples were collected from a herd of 173 goats. The animals were classified as controls (not infected), asymptomatic (seropositives but without detectable CLA clinical signs), and symptomatic (seropositive animals presenting CLA lesions), according to microbiological isolation and immunodiagnosis. The serum samples were analyzed using nuclear magnetic resonance (1H-NMR), nuclear Overhauser effect spectroscopy (NOESY), and Carr-Purcell-Meiboom-Gill (CPMG) sequences. The NMR data were analyzed using chemometrics, and principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were performed to discover specific biomarkers responsible for discrimination between the groups. A high dissemination of the infection by C. pseudotuberculosis was observed, being 74.57% asymptomatic and 11.56% symptomatic. In the evaluation of 62 serum samples by NMR, the techniques were satisfactory in the discrimination of the groups, being also complementary and mutually confirming, demonstrating possible biomarkers for the infection by the bacterium. Twenty metabolites of interest were identified by NOESY and 29 by CPMG, such as tryptophan, polyunsaturated fatty acids, formic acid, NAD+, and 3-hydroxybutyrate, opening promising possibilities for the use of these results in new therapeutic, immunodiagnosis, and immunoprophylactic tools, as well as for studies of the immune response against C. pseudotuberculosis. KEY POINTS: • Sixty-two samples from healthy, CLA asymptomatic, and symptomatic goats were screened • Twenty metabolites of interest were identified by NOESY and 29 by CPMG • 1H-NMR NOESY and CPMG were complementary and mutually confirming.
Collapse
Affiliation(s)
- Jorge Luis Nicoleti
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil
| | - Erik Sobrinho Braga
- Laboratório de Química Biológica, Instituto de Química, Universidade Estadual de Campinas, Campinas, São Paulo State, 13083-970, Brazil
| | - Danijela Stanisic
- Laboratório de Química Biológica, Instituto de Química, Universidade Estadual de Campinas, Campinas, São Paulo State, 13083-970, Brazil
| | - Milka Jadranin
- Laboratório de Química Biológica, Instituto de Química, Universidade Estadual de Campinas, Campinas, São Paulo State, 13083-970, Brazil
- Institute of Chemistry, Technology and Metallurgy, University of Belgrade, 11000, Belgrade, Serbia
| | - Débora Andréa Evangelista Façanha
- Institute of Rural Development, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Redenção, Ceará State, 62790-000, Brazil
| | - Thiago Doria Barral
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil
| | - Samira Abdallah Hanna
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil
| | - Vasco Azevedo
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais State, 31270-901, Brazil
| | - Roberto Meyer
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil
| | - Ljubica Tasic
- Laboratório de Química Biológica, Instituto de Química, Universidade Estadual de Campinas, Campinas, São Paulo State, 13083-970, Brazil
| | - Ricardo Wagner Portela
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil.
| |
Collapse
|
9
|
Armstrong CE, Gilmore AM, Boss PK, Pagay V, Jeffery DW. Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra. Food Chem 2023; 403:134321. [DOI: 10.1016/j.foodchem.2022.134321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 09/15/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022]
|
10
|
Armstrong CEJ, Niimi J, Boss PK, Pagay V, Jeffery DW. Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine. Foods 2023; 12:foods12040757. [PMID: 36832832 PMCID: PMC9955574 DOI: 10.3390/foods12040757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/26/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
Generations of sensors have been developed for predicting food sensory profiles to circumvent the use of a human sensory panel, but a technology that can rapidly predict a suite of sensory attributes from one spectral measurement remains unavailable. Using spectra from grape extracts, this novel study aimed to address this challenge by exploring the use of a machine learning algorithm, extreme gradient boosting (XGBoost), to predict twenty-two wine sensory attribute scores from five sensory stimuli: aroma, colour, taste, flavour, and mouthfeel. Two datasets were obtained from absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy with different fusion methods: variable-level data fusion of absorbance and fluorescence spectral fingerprints, and feature-level data fusion of A-TEEM and CIELAB datasets. The results for externally validated models showed slightly better performance using only A-TEEM data, predicting five out of twenty-two wine sensory attributes with R2 values above 0.7 and fifteen with R2 values above 0.5. Considering the complex biotransformation involved in processing grapes to wine, the ability to predict sensory properties based on underlying chemical composition in this way suggests that the approach could be more broadly applicable to the agri-food sector and other transformed foodstuffs to predict a product's sensory characteristics from raw material spectral attributes.
Collapse
Affiliation(s)
- Claire E. J. Armstrong
- Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
- School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
| | - Jun Niimi
- School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
- CSIRO Agriculture and Food, Locked Bag 2, Glen Osmond, SA 5064, Australia
| | - Paul K. Boss
- Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
- CSIRO Agriculture and Food, Locked Bag 2, Glen Osmond, SA 5064, Australia
| | - Vinay Pagay
- Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
- School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
| | - David W. Jeffery
- Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
- School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
- Correspondence:
| |
Collapse
|
11
|
Kalopesa E, Karyotis K, Tziolas N, Tsakiridis N, Samarinas N, Zalidis G. Estimation of Sugar Content in Wine Grapes via In Situ VNIR-SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031065. [PMID: 36772104 PMCID: PMC9920554 DOI: 10.3390/s23031065] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/04/2023] [Accepted: 01/12/2023] [Indexed: 06/12/2023]
Abstract
Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR-SWIR spectrum (350-2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR-SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.
Collapse
Affiliation(s)
- Eleni Kalopesa
- Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece
| | - Konstantinos Karyotis
- Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece
- School of Science and Technology, International Hellenic University, 14th km Thessaloniki-N. Moudania, 57001 Thermi, Greece
| | - Nikolaos Tziolas
- Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece
| | - Nikolaos Tsakiridis
- Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece
| | - Nikiforos Samarinas
- Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece
| | - George Zalidis
- Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece
| |
Collapse
|
12
|
Viejo CG, Harris N, Fuentes S. Near-infrared spectroscopy analysis of wines through bottles to assess quality traits and provenance. BIO WEB OF CONFERENCES 2023. [DOI: 10.1051/bioconf/20235602003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
Due to increased fraud rates through counterfeiting and adulteration of quality wines, it is important to develop novel non-destructive techniques to assess wine quality and provenance. Therefore, our research group developed a novel method using near-infrared (NIR) spectroscopy (1596-2396 nm) coupled with machine learning (ML) modeling to assess wine vintages and quality traits based on the intensity of sensory descriptors through the bottle. These were developed using samples from an Australian vineyard for Shirazwines. Models resulted in high accuracy 97% for classification (vintages) and R=0.95 regression (sensory quality traits). The proposed method will allow to assess authenticity and sensory quality traits of any wines in the market without the need to open the bottles, which is rapid, accurate, effective, and convenient. Furthermore, currently, there are low-cost NIR devices available in the market with the required spectral range and sensitivity, which can be affordable for winemakers and retailers that can be used with the ML models proposed here.
Collapse
|
13
|
Non-Invasive Digital Technologies to Assess Wine Quality Traits and Provenance through the Bottle. FERMENTATION-BASEL 2022. [DOI: 10.3390/fermentation9010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Due to increased fraud rates through counterfeiting and adulteration of wines, it is important to develop novel non-invasive techniques to assess wine quality and provenance. Assessment of quality traits and provenance of wines is predominantly undertaken with complex chemical analysis and sensory evaluation, which tend to be costly and time-consuming. Therefore, this study aimed to develop a rapid and non-invasive method to assess wine vintages and quality traits using digital technologies. Samples from thirteen vintages from Dookie, Victoria, Australia (2000–2021) of Shiraz were analysed using near-infrared spectroscopy (NIR) through unopened bottles to assess the wine chemical fingerprinting. Three highly accurate machine learning (ML) models were developed using the NIR absorbance values as inputs to predict (i) wine vintage (Model 1; 97.2%), (ii) intensity of sensory descriptors (Model 2; R = 0.95), and (iii) peak area of volatile aromatic compounds (Model 3; R = 0.88). The proposed method will allow the assessment of provenance and quality traits of wines without the need to open the wine bottle, which may also be used to detect wine fraud and provenance. Furthermore, low-cost NIR devices are available in the market with required spectral range and sensitivity, which can be affordable for winemakers and retailers and can be used with the machine learning models proposed here.
Collapse
|
14
|
Ranaweera RK, Bastian SE, Gilmore AM, Capone DL, Jeffery DW. Absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) with multi-block data analysis and machine learning for accurate intraregional classification of Barossa Shiraz wine. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
|
15
|
Pan T, Li J, Fu C, Chang N, Chen J. Visible and Near-Infrared Spectroscopy Combined With Bayes Classifier Based on Wavelength Model Optimization Applied to Wine Multibrand Identification. Front Nutr 2022; 9:796463. [PMID: 35928849 PMCID: PMC9344138 DOI: 10.3389/fnut.2022.796463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 06/13/2022] [Indexed: 11/26/2022] Open
Abstract
The identification of high-quality wine brands can avoid adulteration and fraud and protect the rights and interests of producers and consumers. Since the main components of wine are roughly the same, the characteristic components that can distinguish wine brands are usually trace amounts and not unique. The conventional quantitative detection method for brand identification is complicated and difficult. The naive Bayes (NB) classifier is an algorithm based on probability distribution, which is simple and particularly suitable for multiclass discriminant analysis. However, the absorbance probability between spectral wavelengths is not necessarily strongly independent, which limits the application of Bayes method in spectral pattern recognition. This research proposed a Bayes classifier algorithm based on wavelength optimization. First, a large-scale wavelength screening for equidistant combination (EC) was performed, and then wavelength step-by-step phase-out (WSP) was carried out to reduce the correlation between wavelengths and improve the accuracy of Bayes discrimination. The proposed EC-WSP-Bayes method was applied to the 5-category discriminant analysis of wine brand identification based on visible and near-infrared (Vis-NIR) spectroscopy. Among them, four types of wine brands were collected from regular sales channels as identification brands. The fifth type of samples was composed of 21 other commercial brand wines and home-brewed wines from various sources, as the interference brand. The optimal EC-WSP-Bayes model was selected, the corresponding wavelength combination was 404, 600, 992, 2,070, 2,266, and 2,462 nm located in the visible light, shortwave NIR, and combination frequency regions. In modeling and independent validation, the total recognition accuracy rate (RARTotal) reached 98.1 and 97.6%, respectively. The technology is quick and easy, which is of great significance to regulate the alcohol market. The proposed model of less-wavelength and high-efficiency (N = 6) can provide a valuable reference for small special instruments. The proposed integrated chemometric method can reduce the correlation between wavelengths, improve the recognition accuracy, and improve the applicability of the Bayesian method.
Collapse
Affiliation(s)
- Tao Pan
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, China
- *Correspondence: Tao Pan,
| | - Jiaqi Li
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, China
| | - Chunli Fu
- Department of Biological Engineering, Jinan University, Guangzhou, China
| | - Nailiang Chang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, China
| | - Jiemei Chen
- Department of Biological Engineering, Jinan University, Guangzhou, China
| |
Collapse
|
16
|
Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition. SENSORS 2022; 22:s22062132. [PMID: 35336301 PMCID: PMC8950699 DOI: 10.3390/s22062132] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/05/2022] [Accepted: 03/07/2022] [Indexed: 02/04/2023]
Abstract
Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.
Collapse
|
17
|
Xagoraris M, Revelou PK, Arvanitis N, Basalekou M, Pappas CS, Tarantilis PA. The application of right-angle fluorescence spectroscopy as a tool to distinguish five autochthonous commercial Greek white wines. Curr Res Food Sci 2021; 4:815-820. [PMID: 34825196 PMCID: PMC8604742 DOI: 10.1016/j.crfs.2021.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/07/2021] [Accepted: 11/07/2021] [Indexed: 11/20/2022] Open
Abstract
White wine is among the most widely consumed alcoholic beverages. Varietal discrimination of wines has received increasing attention. Today's consumers require a sense of authenticity and are deterred by falsehood or misrepresentation in product marketing. However, wine can involve various types of frauds, which directly affects the distribution of wine in national and international markets. Right-angle fluorescence spectroscopy is a simple and rapid analytical technique that in combination with chemometric algorithms, constitutes a novel method for wine authentication. In this study, the stepwise-Linear Discriminant Analysis algorithm was applied in three representative spectral regions related to phenolic compounds for the purpose of distinguishing white wines according to the grape variety. The wavelength at 310 nm attributed to the hydroxycinnamic acids and stilbene provided a higher classification rate (95.5%) than the λex 280 and 295 nm regions (79.8%), suggesting that these compounds are highly related to the botanical origin of samples. The chemometric models were validated utilizing cross-validation and an external validation set to enhance the robustness of the proposed methodology. The above-mentioned methodology constitutes a powerful tool for the varietal discrimination of white wines and can be used in industrial setting. The ultimate goal of this study is to contribute to the efforts towards the authentication of Greek white wine which will eventually support producers and suppliers to remain competitive and simultaneously protect the consumers from fraudulent practices.
Collapse
Affiliation(s)
- Marinos Xagoraris
- Laboratory of Chemistry, Department of Food Science and Human Nutrition. Agricultural University of Athens, 75 Iera Odos, 11855, Athens, Greece
| | - Panagiota-Kyriaki Revelou
- Laboratory of Chemistry, Department of Food Science and Human Nutrition. Agricultural University of Athens, 75 Iera Odos, 11855, Athens, Greece
- Department of Food Science and Technology, University of West Attica, Ag. Spyridonos Str, 12243, Egaleo, Athens, Greece
| | - Nikos Arvanitis
- Laboratory of Chemistry, Department of Food Science and Human Nutrition. Agricultural University of Athens, 75 Iera Odos, 11855, Athens, Greece
| | - Marianthi Basalekou
- Laboratory of Chemistry, Department of Food Science and Human Nutrition. Agricultural University of Athens, 75 Iera Odos, 11855, Athens, Greece
- Department of Wine, Vine and Beverage Sciences, University of West Attica, Ag. Spyridona Street, 12243, Aigaleo, Athens, Greece
| | - Christos S. Pappas
- Laboratory of Chemistry, Department of Food Science and Human Nutrition. Agricultural University of Athens, 75 Iera Odos, 11855, Athens, Greece
| | - Petros A. Tarantilis
- Laboratory of Chemistry, Department of Food Science and Human Nutrition. Agricultural University of Athens, 75 Iera Odos, 11855, Athens, Greece
| |
Collapse
|