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Han H, Sha R, Dai J, Wang Z, Mao J, Cai M. Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information. Foods 2024; 13:1016. [PMID: 38611322 PMCID: PMC11012206 DOI: 10.3390/foods13071016] [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: 03/06/2024] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky-Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.
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Affiliation(s)
- Hao Han
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Ruyi Sha
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Jing Dai
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Zhenzhen Wang
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Jianwei Mao
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
| | - Min Cai
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (H.H.); (J.D.); (Z.W.); (J.M.); (M.C.)
- Zhejiang Provincial Key Laboratory for Chemical & Biological Processing Technology of Farm Product, Hangzhou 310023, China
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Guo M, Wang K, Lin H, Wang L, Cao L, Sui J. Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives. Compr Rev Food Sci Food Saf 2024; 23:e13301. [PMID: 38284587 DOI: 10.1111/1541-4337.13301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/27/2023] [Accepted: 12/31/2023] [Indexed: 01/30/2024]
Abstract
In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
- College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Lei Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
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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.
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Azcarate SM, Ríos-Reina R, Amigo JM, Goicoechea HC. Data handling in data fusion: Methodologies and applications. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116355] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Abstract
This paper is focused on the assessment of a multi-sensor approach to improve the overall characterization of sparkling wines (cava wines). Multi-sensor, low-level data fusion can provide more comprehensive and more accurate vision of results compared with the study of simpler data sets from individual techniques. Data from different instrumental platforms were combined in an enriched matrix, integrating information from spectroscopic (UV/Vis and FTIR), chromatographic, and other techniques. Sparkling wines belonging to different classes, which differed in the grape varieties, coupages, and wine-making processes, were analyzed to determine organic acids (e.g., tartaric, lactic, malic, and acetic acids), pH, total acidity, polyphenols, total antioxidant capacity, ethanol, or reducing sugars. The resulting compositional values were treated chemometrically for a more efficient recovery of the underlaying information. In this regard, exploratory methods such as principal component analysis showed that phenolic compounds were dependent on varietal and blending issues while organic acids were more affected by fermentation features. The analysis of the multi-sensor data set provided a more comprehensive description of cavas according to grape classes, blends, and vinification processes. Hierarchical Cluster Analysis (HCA) allowed specific groups of samples to be distinguished, featuring malolactic fermentation and the chardonnay and red grape classes. Partial Least Squares-Discriminant Analysis (PLS-DA) also classified samples according to the type of grape varieties and fermentations. Bar charts and complementary statistic test were performed to better define the differences among the studied samples based on the most significant markers of each cava wine type. As a conclusion, catechin, gallic, gentisic, caftaric, caffeic, malic, and lactic acids were the most remarkable descriptors that contributed to their discrimination based on varietal, blending, and oenological factors.
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Mutz YS, Rosario DKA, Conte-Junior CA. Insights into chemical and sensorial aspects to understand and manage beer aging using chemometrics. Compr Rev Food Sci Food Saf 2020; 19:3774-3801. [PMID: 33337064 DOI: 10.1111/1541-4337.12642] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 08/28/2020] [Accepted: 09/03/2020] [Indexed: 12/15/2022]
Abstract
Beer chemical instability remains, at present, the main challenge in maintaining beer quality. Although not fully understood, after decades of research, significant progress has been made in identifying "aging compounds," their origin, and formation pathways. However, as the nature of aging relies on beer manufacturing aspects such as raw materials, process variables, and storage conditions, the chemical profile differs among beers. Current research points to the impact of nonoxidative reactions on beer quality. The effect of Maillard and Maillard intermediates on the final beer quality has become the focus of beer aging research, as prevention of oxidation can only sustain beer quality to some extent. On the other hand, few studies have focused on tracing a profile of whose compound is sensory relevant to specific types of beer. In this matter, the incorporation of "chemometrics," a class of multivariate statistic procedures, has helped brewing scientists achieve specific correlations between the sensory profile and chemical data. The use of chemometrics as exploratory data analysis, discrimination techniques, and multivariate calibration techniques has made the qualitatively and quantitatively translation of sensory perception of aging into manageable chemical and analytical parameters. However, despite their vast potential, these techniques are rarely employed in beer aging studies. This review discusses the chemical and sensorial bases of beer aging. It focuses on how chemometrics can be used to their full potential, with future perspectives and research to be incorporated in the field, enabling a deeper and more specific understanding of the beer aging picture.
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Affiliation(s)
- Yhan S Mutz
- Post Graduate Program in Food Science, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,Center for Food Analysis, Technological Development Support Laboratory (LADETEC), Avenida Horácio Macedo, Rio de Janeiro, Brazil
| | - Denes K A Rosario
- Post Graduate Program in Food Science, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,Center for Food Analysis, Technological Development Support Laboratory (LADETEC), Avenida Horácio Macedo, Rio de Janeiro, Brazil
| | - Carlos A Conte-Junior
- Post Graduate Program in Food Science, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,Post Graduate Program in Veterinary Hygiene, Faculty of Veterinary Medicine, Fluminense Federal University, Niterói, Brazil.,Center for Food Analysis, Technological Development Support Laboratory (LADETEC), Avenida Horácio Macedo, Rio de Janeiro, Brazil.,National Institute of Health Quality Control, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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