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Pastor-Vega N, Carbonero-Pacheco J, Mauricio JC, Moreno J, García-Martínez T, Nitin N, Ogawa M, Rai R, Moreno-García J. Flor yeast immobilization in microbial biocapsules for Sherry wine production: microvinification approach. World J Microbiol Biotechnol 2023; 39:271. [PMID: 37541980 PMCID: PMC10403390 DOI: 10.1007/s11274-023-03713-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/23/2023] [Indexed: 08/06/2023]
Abstract
Sherry wine is a pale-yellowish dry wine produced in Southern-Spain which features are mainly due to biological aging when the metabolism of biofilm-forming yeasts (flor yeasts) consumes ethanol (and other non-fermentable carbon sources) from a previous alcoholic fermentation, and produces volatile compounds such as acetaldehyde. To start aging and maintain the wine stability, a high alcohol content is required, which is achieved by the previous fermentation or by adding ethanol (fortification). Here, an alternative method is proposed which aims to produce a more economic, distinctive Sherry wine without fortification. For this, a flor yeast has been pre-acclimatized to glycerol consumption against ethanol, and later confined in a fungal-based immobilization system known as "microbial biocapsules", to facilitate its inoculum. Once aged, the wines produced using biocapsules and free yeasts (the conventional method) exhibited chemical differences in terms of acidity and volatile concentrations. These differences were evaluated positively by a sensory panel. Pre-acclimatization of flor yeasts to glycerol consumption was not successful but when cells were immobilized in fungal pellets, ethanol consumption was lower. We believe that immobilization of flor yeasts in microbial biocapsules is an economic technique that can be used to produce high quality differentiated Sherry wines.
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Affiliation(s)
- Noelia Pastor-Vega
- Department of Agricultural Chemistry, Edaphology and Microbiology, University of Córdoba, Córdoba, 14014 Spain
| | - Juan Carbonero-Pacheco
- Department of Agricultural Chemistry, Edaphology and Microbiology, University of Córdoba, Córdoba, 14014 Spain
| | - Juan Carlos Mauricio
- Department of Agricultural Chemistry, Edaphology and Microbiology, University of Córdoba, Córdoba, 14014 Spain
| | - Juan Moreno
- Department of Agricultural Chemistry, Edaphology and Microbiology, University of Córdoba, Córdoba, 14014 Spain
| | - Teresa García-Martínez
- Department of Agricultural Chemistry, Edaphology and Microbiology, University of Córdoba, Córdoba, 14014 Spain
| | - Nitin Nitin
- Department of Food Science and Technology, University of California, Davis, Davis, CA 95616 USA
| | - Minami Ogawa
- Department of Food Science and Technology, University of California, Davis, Davis, CA 95616 USA
| | - Rewa Rai
- Department of Food Science and Technology, University of California, Davis, Davis, CA 95616 USA
| | - Jaime Moreno-García
- Department of Agricultural Chemistry, Edaphology and Microbiology, University of Córdoba, Córdoba, 14014 Spain
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2
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Zhou FY, Liang J, Lü YL, Kuang HX, Xia YG. A nondestructive solution to quantify monosaccharides by ATR-FTIR and multivariate regressions: A case study of Atractylodes polysaccharides. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 279:121411. [PMID: 35653809 DOI: 10.1016/j.saa.2022.121411] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/12/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
The quality evaluation of nature polysaccharides is a tough nut to crack because of its high Mw distributions and larger polarity property. It is well-known that infrared spectroscopy and multiple regression modeling have been used for quantitative examinations in multiple fields, but it has not been applied to the compositional analysis of polysaccharides. In this study, attenuated total reflectance-fourier transform infrared spectroscopy is used to simultaneously quantify aldoses, ketose and uronic acids in Atractylodes polysaccharides by a combination of multivariate regressions. After experience of different data processing pretreatments, the resulting spectrum contains maximum amount of information of monosaccharide contents in Atractylodes polysaccharides. In this case, different smoothing points, derivatives, SNV and MSC are used in the pre-modeling spectrum processing and VIP screening is used to reduce the number of variables to simplify the calculation of the model. All the most optimal prediction models have both good prediction ability (R2 ≥ 0.9 and RPD > 3) and no over fitting (RMSEP/RMSEC < 3). This strategy has opened a new possibility for the nondestructive determination of complex monosaccharide compositions of natural polysaccharides in a short detection time, low equipment requirement and high experimental safety.
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Affiliation(s)
- Fang-Yu Zhou
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, 24 Heping Road, Harbin 150040, PR China
| | - Jun Liang
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, 24 Heping Road, Harbin 150040, PR China
| | - Yan-Li Lü
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, 24 Heping Road, Harbin 150040, PR China
| | - Hai-Xue Kuang
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, 24 Heping Road, Harbin 150040, PR China
| | - Yong-Gang Xia
- Key Laboratory of Basic and Application Research of Beiyao (Heilongjiang University of Chinese Medicine), Ministry of Education, 24 Heping Road, Harbin 150040, PR China.
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3
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Mid-infrared and near-infrared spectroscopies to classify improper fermentation of pineapple wine. CHEMICAL PAPERS 2022. [DOI: 10.1007/s11696-022-02472-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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4
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Wang Z, Zhang L, Li Y, Liu Q, Chunlong Y. Non-acylated and acylated anthocynins in red wines of different ages: Color contribution and Evaluation. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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5
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Casian T, Nagy B, Kovács B, Galata DL, Hirsch E, Farkas A. Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review. Molecules 2022; 27:4846. [PMID: 35956791 PMCID: PMC9369811 DOI: 10.3390/molecules27154846] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.
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Affiliation(s)
- Tibor Casian
- Department of Pharmaceutical Technology and Biopharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania;
| | - Brigitta Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary; (D.L.G.); (E.H.); (A.F.)
| | - Béla Kovács
- Department of Biochemistry and Environmental Chemistry, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania;
| | - Dorián László Galata
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary; (D.L.G.); (E.H.); (A.F.)
| | - Edit Hirsch
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary; (D.L.G.); (E.H.); (A.F.)
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, H-1111 Budapest, Hungary; (D.L.G.); (E.H.); (A.F.)
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Junges CH, Guerra CC, Reis NA, Gomes AA, Diogo FS, Ferrão MF. GRAPE JUICE CLASSIFICATION WITH RESPECT AGRICULTURAL PRODUCTION SYSTEM BY MEANS OF VISIBLE SPECTROSCOPY CHEMOMETRICS ASSISTED. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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7
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Lambrecht K, Nieuwoudt H, du Toit W, Aleixandre-Tudo JL. Moving towards in-line monitoring of phenolic extraction during red wine fermentations using infra-red spectroscopy technology. Influence of sample preparation and instrumentation. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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8
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Stój A, Czernecki T, Sosnowska B, Niemczynowicz A, Matwijczuk A. Impact of Grape Variety, Yeast and Malolactic Fermentation on Volatile Compounds and Fourier Transform Infrared Spectra in Red Wines. POL J FOOD NUTR SCI 2022. [DOI: 10.31883/pjfns/145665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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9
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Classification and authentication of Slovak varietal wines by attenuated total reflectance Fourier-transform infrared spectrometry and multidimensional data analysis. CHEMICAL PAPERS 2022. [DOI: 10.1007/s11696-021-02041-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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10
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11
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Gomes AA, Khvalbota L, Machyňáková A, Furdíková K, Zini CA, Špánik I. Slovak Tokaj wines classification with respect to geographical origin by means of one class approaches. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 257:119770. [PMID: 33852999 DOI: 10.1016/j.saa.2021.119770] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/21/2021] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
Tokaj wines could be produced only in so called Tokaj/Tokay wine region that falls within two countries Slovakia and Hungary. Thus, wines bearing Tokaj appellation must be produced only in Hungary and Slovakia by traditional process. Unfortunately, some counterfeit wines from neighbour region in Ukraine could be found in market. The aim of this work is to explore a simple UV-VIS spectrum to recognise true Tokaj/Tokay wines from counterfeits and try to differentiate wines based on their country of origin. This type of question can be duly answered using one class classification approach. Two different approaches, Data Driven Soft Independent Modelling of Class Analogy - DD-SIMCA and One-Class Partial Least Squares - OC-PLS were tested and evaluated for this purpose. In both cases, rigorous way models were built and optimized using only samples of the target class. A set of external samples containing samples from target class and non-target were used to validate the models ability to recognize Slovak samples and reject non-Slovak samples. Model based on DD-SIMCA showed better performance (97% correct rating) compared to OC-PLS models (80% correct rating). Comparing both approaches in terms of sensitivity and specificity, both exhibit high sensitivity (low false negative rate: DD-SIMCA 95% and OC-PLS 100%), however the OC-PLS based model showed low specificity (40%) while DD-SIMCA showed high specificity (100%) rejecting all samples out of Slovak origin. Therefore, the results found in this study show that it is possible to successfully combine UV-VIS spectra and DD-SIMCA models to discriminate Tokaj wine samples of Slovak origin from others. Equally important is environmentally friendly (fast, simple, absence of solvents) classification method in line with green chemistry.
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Affiliation(s)
- Adriano A Gomes
- Instituto de Química, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil.
| | - Liudmyla Khvalbota
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia
| | - Andrea Machyňáková
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia
| | - Katarína Furdíková
- Institute of Biotechnology, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia
| | - Claudia A Zini
- Instituto de Química, Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil
| | - Ivan Špánik
- Institute of Analytical Chemistry, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 812 37 Bratislava, Slovakia.
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12
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Calle JLP, Ferreiro-González M, Ruiz-Rodríguez A, Barbero GF, Álvarez JÁ, Palma M, Ayuso J. A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars. Foods 2021; 10:foods10061411. [PMID: 34207095 PMCID: PMC8233915 DOI: 10.3390/foods10061411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 11/16/2022] Open
Abstract
Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.
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Affiliation(s)
- José Luis P. Calle
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
| | - Marta Ferreiro-González
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
- Correspondence: ; Tel.: +34-956-01-6359
| | - Ana Ruiz-Rodríguez
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
| | - Gerardo F. Barbero
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
| | - José Á. Álvarez
- Department of Physical Chemistry, Faculty of Sciences, Institute of Biomolecules (INBIO), University of Cadiz, 11510 Puerto Real, Spain; (J.Á.Á.); (J.A.)
| | - Miguel Palma
- Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain; (J.L.P.C.); (A.R.-R.); (G.F.B.); (M.P.)
| | - Jesús Ayuso
- Department of Physical Chemistry, Faculty of Sciences, Institute of Biomolecules (INBIO), University of Cadiz, 11510 Puerto Real, Spain; (J.Á.Á.); (J.A.)
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Mendes E, Duarte N. Mid-Infrared Spectroscopy as a Valuable Tool to Tackle Food Analysis: A Literature Review on Coffee, Dairies, Honey, Olive Oil and Wine. Foods 2021; 10:foods10020477. [PMID: 33671755 PMCID: PMC7926530 DOI: 10.3390/foods10020477] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/15/2021] [Accepted: 02/17/2021] [Indexed: 12/12/2022] Open
Abstract
Nowadays, food adulteration and authentication are topics of utmost importance for consumers, food producers, business operators and regulatory agencies. Therefore, there is an increasing search for rapid, robust and accurate analytical techniques to determine the authenticity and to detect adulteration and misrepresentation. Mid-infrared spectroscopy (MIR), often associated with chemometric techniques, offers a fast and accurate method to detect and predict food adulteration based on the fingerprint characteristics of the food matrix. In the first part of this review the basic concepts of infrared spectroscopy, sampling techniques, as well as an overview of chemometric tools are summarized. In the second part, recent applications of MIR spectroscopy to the analysis of foods such as coffee, dairy products, honey, olive oil and wine are discussed, covering a timespan from 2010 to mid-2020. The literature gathered in this article clearly reveals that the MIR spectroscopy associated with attenuated total reflection acquisition mode and different chemometric tools have been broadly applied to address quality, authenticity and adulteration issues. This technique has the advantages of being simple, fast and easy to use, non-destructive, environmentally friendly and, in the future, it can be applied in routine analyses and official food control.
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14
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Summerson V, Gonzalez Viejo C, Szeto C, Wilkinson KL, Torrico DD, Pang A, De Bei R, Fuentes S. Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms. SENSORS 2020; 20:s20185099. [PMID: 32906800 PMCID: PMC7571113 DOI: 10.3390/s20185099] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 09/05/2020] [Accepted: 09/05/2020] [Indexed: 02/06/2023]
Abstract
Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.
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Affiliation(s)
- Vasiliki Summerson
- Digital Agriculture, Food, and Wine Group, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; (V.S.); (C.G.V.); (A.P.)
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food, and Wine Group, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; (V.S.); (C.G.V.); (A.P.)
| | - Colleen Szeto
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1, Glen Osmond, SA 5064, Australia; (C.S.); (K.L.W.); (R.D.B.)
- The Australian Research Council Training Centre for Innovative Wine Production, PMB 1, Glen Osmond, SA 5064, Australia
| | - Kerry L. Wilkinson
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1, Glen Osmond, SA 5064, Australia; (C.S.); (K.L.W.); (R.D.B.)
- The Australian Research Council Training Centre for Innovative Wine Production, PMB 1, Glen Osmond, SA 5064, Australia
| | - Damir D. Torrico
- Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, Canterbury, New Zealand;
| | - Alexis Pang
- Digital Agriculture, Food, and Wine Group, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; (V.S.); (C.G.V.); (A.P.)
| | - Roberta De Bei
- School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1, Glen Osmond, SA 5064, Australia; (C.S.); (K.L.W.); (R.D.B.)
| | - Sigfredo Fuentes
- Digital Agriculture, Food, and Wine Group, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia; (V.S.); (C.G.V.); (A.P.)
- Correspondence:
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15
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Could antioxidant capacity and flavonoid content of ethanolic extracts of geopropolis from Brazilian native bees be estimated from digital photos and NIR Spectra? Microchem J 2020. [DOI: 10.1016/j.microc.2020.105031] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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16
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Abstract
Fourier transform infrared spectroscopy (FT-IR) has gained popularity in the wine sector due to its simplicity and ability to provide a wine’s fingerprint. For this reason, it is often used for authentication and traceability purposes with more than satisfactory results. In this review, an outline of the reasons why authenticity and traceability are important to the wine sector is given, along with a brief overview of the analytical methods used for their attainment; statistical issues and compounds, on which authentication usually is based, are discussed. Moreover, insight on the mode of action of FT-IR is given, along with successful examples from its use in different areas of interest for classification. Finally, prospects and challenges for suggested future research are given. For more accurate and effective analyses, the construction of a large database consisting of wines from different regions, varieties and winemaking protocols is suggested.
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17
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Analysis of red wines using an electronic tongue and infrared spectroscopy. Correlations with phenolic content and color parameters. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2019.108785] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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18
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Geană EI, Ciucure CT, Apetrei C, Artem V. Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination. Molecules 2019; 24:molecules24224166. [PMID: 31744212 PMCID: PMC6891476 DOI: 10.3390/molecules24224166] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 11/15/2019] [Indexed: 12/24/2022] Open
Abstract
One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.
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Affiliation(s)
- Elisabeta-Irina Geană
- National R&D Institute for Cryogenics and Isotopic Technologies—ICIT Rm. Valcea, 4th Uzinei Street, PO Raureni, Box 7, 240050 Rm. Valcea, Romania; (E.-I.G.); (C.T.C.)
| | - Corina Teodora Ciucure
- National R&D Institute for Cryogenics and Isotopic Technologies—ICIT Rm. Valcea, 4th Uzinei Street, PO Raureni, Box 7, 240050 Rm. Valcea, Romania; (E.-I.G.); (C.T.C.)
| | - Constantin Apetrei
- Physics and Environment, Department of Chemistry, Faculty of Science and Environment, “Dunarea de Jos” University of Galati, 111 Domneasca Street, RO-800008 Galati, Romania
- Correspondence: ; Tel.: +40-727-580-914
| | - Victoria Artem
- Research Station for Viticulture and Oenology Murfatlar, Calea Bucuresti str., no. 2, Murfatlar, 905100 Constanta, Romania;
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Chen X, Sun X, Hua H, Yi Y, Li H, Chen C. Quality evaluation of decoction pieces of Rhizoma Atractylodis Macrocephalae by near infrared spectroscopy coupled with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 221:117169. [PMID: 31174137 DOI: 10.1016/j.saa.2019.117169] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 04/29/2019] [Accepted: 05/26/2019] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To establish a fast, simple and reliable method for quality evaluation of decoction pieces of Rhizoma Atractylodis Macrocephalae (referred as BZ below) by near infrared spectroscopy coupled with chemometrics. METHOD Twelve batches of raw medicinal materials of BZ were collected from three main producing location in China. According to the Pharmacopoeia of the People's Republic of China, these raw decoction pieces were stir-fried in wheat bran using a stir-frying machine for 3, 6, 9, 12 and 15 min, respectively. The resulted 60 samples were categorized into three classes (i.e., light, moderate and dark) by experienced pharmacists according to their surface color. After that, these slices were smashed to acquire near infrared spectra and to determine the contents of atractylenolide I, II and III by HPLC method. Qualitative and quantitative models were constructed to relate the spectra to the color labels and to the contents of three atractylenolides. Various chemometrics methods, including calibration methods like principal component analysis, partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR), spectra pretreatment methods like standard normal variate, multiplicative scatter correction, derivation and smoothing, feature selection methods like particle swarm optimization, genetic algorithm (GA) and other fourteen methods were compared in detail. The PLS-DA models were evaluated by jackknife tests with calculating parameters such as error rate (ERR), true positive rate (TPR), true negative rate (TNR) and F1 score, meanwhile the PLSR models were evaluated by five fold cross-validation tests with calculating parameters such as coefficients of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). RESULTS The PLS-DA models with spectra pretreated by 1D5S or 1D9S and wavelengths selected by InfFS, Relief-F, MutInfFS, fisher or CFS performed best, yielding 0.00 of ERR, 1.00 of TPR, 1.00 of TNR, and 1.00 of F1 for all three classes. As for quantitative models, the PLSR models by 1D5S spectra pretreatment and GA wavelengths selection performed best, where R2C and R2P were all >0.95, RMSEC and RMSEP were all <0.04%, MAEC and MAEP were all <0.04%, and RPD were all >5. CONCLUSION The present qualitative and quantitative models can be successfully used to distinguish the degree of suitability of processed BZ, and to determine the contents of three atractylenolides, which thus are of great help for quality evaluation and control of processed BZ and other decoction pieces.
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Affiliation(s)
- Xiaoyi Chen
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
| | - Xuefen Sun
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
| | - Haimin Hua
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
| | - Yuan Yi
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
| | - Huiling Li
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
| | - Chao Chen
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China.
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