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Junges CH, Guerra CC, Gomes AA, Ferrão MF. Multiblock data applied in organic grape juice authentication by one-class classification OC-PLS. Food Chem 2024; 436:137695. [PMID: 37857206 DOI: 10.1016/j.foodchem.2023.137695] [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: 06/08/2023] [Revised: 09/27/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023]
Abstract
A new strategy has been developed to enhance the assessment of the authenticity of whole grape juice within the organic class. This approach is based on the analysis of data from different analytical sources. The novel method employs a multiblock regression technique, specifically the one-class partial least squares (OC-PLS) classifier, to establish a relationship between each predictor block and the response variable. Sequential calculations are performed after orthogonalization with respect to the preceding regression scores. The proposed method has demonstrated effectiveness in detecting targeted samples. The results achieved of the best models for the test set had rates of up to 100 % sensitivity, 89 % specificity, and 83 % accuracy. To compare with the multiblock models, the DD-SIMCA method was employed, but it yielded inferior results when applied to visible data. The multiblock approach proved to be efficient in evaluating from different datasets of varied sources to classification of organic grape juice.
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Affiliation(s)
- Carlos H Junges
- Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil.
| | - Celito C Guerra
- Laboratório de Cromatografia e Espectrometria de Massas (LACEM), Unidade Uva e Vinho, Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), Rua Livramento, 515, Bento Gonçalves, Rio Grande do Sul, CEP 95701-008, Brazil
| | - Adriano A Gomes
- Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil
| | - Marco F Ferrão
- Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil; Instituto Nacional de Ciência e Tecnologia-Bioanalítica (INCT-Bioanalítica), Cidade Universitária Zeferino Vaz, s/n, Campinas, São Paulo (SP), CEP 13083-970, Brazil
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Wang Y, Ren Y, Kang S, Yin C, Shi Y, Men H. Identification of tea quality at different picking periods: A hyperspectral system coupled with a multibranch kernel attention network. Food Chem 2024; 433:137307. [PMID: 37683489 DOI: 10.1016/j.foodchem.2023.137307] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/02/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023]
Abstract
The material content and nutritional composition of tea vary during different picking periods, leading to variations in tea quality. The absence of rapid evaluation methods for identifying tea quality at different picking periods hinders the smooth operation and maintenance of agricultural production and market sales. In this work, hyperspectral technology combined with the multibranch kernel attention network (MBKA-Net) is proposed to identify the overall quality of tea during different picking periods. First, spectral information of six different tea picking periods is obtained using a hyperspectral system. Second, the multibranch kernel attention (MBKA) method is proposed, which effectively mines spectral features through multiscale adaptive extraction and achieves classification of tea at different picking periods. Finally, MBKA-Net achieves outstanding performance with 96.18% accuracy, 97.14% precision, and 97.18% recall. In conclusion, MBKA-Net combined with a hyperspectral system provides an effective detection method for identifying the quality of tea at different picking periods.
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Affiliation(s)
- Yanwei Wang
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Institute of Advanced Sensor Technology, Northeast Electric Power University, Jilin 132012, China.
| | - Yuqi Ren
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Institute of Advanced Sensor Technology, Northeast Electric Power University, Jilin 132012, China.
| | - Siyuan Kang
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Institute of Advanced Sensor Technology, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Chongbo Yin
- School of Bioengineering, Chongqing University, Chongqing 400044, China.
| | - Yan Shi
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Institute of Advanced Sensor Technology, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.
| | - Hong Men
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Institute of Advanced Sensor Technology, Northeast Electric Power University, Jilin 132012, China.
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Varga T, Molnár M, Molnár A, Jull AT, Palcsu L, László E. Radiocarbon dating of microliter sized Hungarian Tokaj wine samples. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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Ferreira SL, Scarminio IS, Veras G, Bezerra MA, da Silva Junior JB. Special issue – XI Brazilian Chemometrics Workshop Preface. Food Chem 2022; 390:133113. [DOI: 10.1016/j.foodchem.2022.133113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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Handling multiblock data in wine authenticity by sequentially orthogonalized one class partial least squares. Food Chem 2022; 382:132271. [PMID: 35189444 DOI: 10.1016/j.foodchem.2022.132271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 11/23/2022]
Abstract
New approach to deal with food authentication by modelling methods based on data recorded from different sources is proposed and called OC-PLS, combines an orthogonalization step between the different data sets to eliminate redundant information followed by definition of an acceptance area for a target class by OC-PLS. The proposed method was evaluated in two case studies. The first study used a controlled scenario with simulated data. In the second case study, the approach was applied using UV-VIS and IR data, in order to differentiate Slovak Tokaj Selection wines of high quality from other lower market value wines from the Slovak Tokaj wine region. In both cases, better results were reached than when individual blocks of data were achieved. The proposed method proved to be effective in properly exploring common and distinct information in each data block. The best compromise between sensitivity and selectivity in the prediction step was achieved.
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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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