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Rong Y, Riaz T, Lin H, Wang Z, Chen Q, Ouyang Q. Application of visible near-infrared spectroscopy combined with colorimetric sensor array for the aroma quality evaluation in tencha drying process. Spectrochim Acta A Mol Biomol Spectrosc 2024; 304:123385. [PMID: 37714101 DOI: 10.1016/j.saa.2023.123385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/31/2023] [Accepted: 09/08/2023] [Indexed: 09/17/2023]
Abstract
The drying process is a critical stage in developing the aroma quality of tencha. In our research, visible near infrared (Vis-NIR) and colorimetric sensor array (Vis-NIR-CSA) were used for evaluating the aroma quality of tencha drying process. Vis-NIR recorded the spectral signal of CSA after the reaction in samples. Subsequently, the aroma quality was predicted by a combination of different data fusion strategies and classification and regression tree (CART) in tencha drying process. The high-level fusion strategy showed the best performance, with calibration and prediction set accuracy of 94.68% and 93.48%, respectively. The results indicated that Vis-NIR-CSA combined with high-level data fusion could be applied satisfactorily in the aroma quality evaluation of tencha. Moreover, pentanal was identified to be highly correlated with aroma quality during tencha drying process, which verified the sensor identification results. This study contributed to controlling good manufacturing practices and designing optimal tencha processing systems.
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Affiliation(s)
- Yanna Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Tahreem Riaz
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Zhen Wang
- National Research and Development Center for Matcha Processing Technology, Jiangsu Xinpin Tea Co., Ltd, Changzhou 213254, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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Pallottino F, Stazi SR, D'Annibale A, Marabottini R, Allevato E, Antonucci F, Costa C, Moscatelli MC, Menesatti P. Rapid assessment of As and other elements in naturally-contaminated calcareous soil through hyperspectral VIS-NIR analysis. Talanta 2018; 190:167-173. [PMID: 30172494 DOI: 10.1016/j.talanta.2018.07.082] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 07/23/2018] [Accepted: 07/26/2018] [Indexed: 11/24/2022]
Abstract
Although arsenic (As) toxicity in soil vary depending on its chemical forms and oxidation states, regulatory limits for this compartment rely on total As content. Conventional methods of total As determination are expensive and time-consuming. The development of predictive techniques might enable a speditive assessment of As contamination in those scenarios, such as thermal spring sites, where exposure to the metalloid poses a threat to human health. The objective of this study was to assess the suitability of Visible Near Infrared spectrophotometry for predicting the total As content in highly calcareous thermal spring soils and the same aim was pursued for those elements (i.e. Al, Fe and Mn) the chemistry of which is tightly connected with that of As. A Partial Least Square approach, including cross-validation and external independent test, was used to relate the concentrations of the target elements to spectral data. The most accurate prediction was found for As with Pearson's coefficient, RMSE, RPD and SEP being equal to 0.94, 69.65, 2.9 and 66.99, respectively. Less accurate predictions were found for Al (r = 0.88; RMSE = 11014; RPD = 1.96; SEP = 11014), Fe (r = 0.93; RMSE = 6921.1; RPD = 2.45; SEP = 6462.4), and Mn (r = 0.92; RMSE = 542.01; RPD = 2.43; SEP = 529.79).
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Affiliation(s)
- F Pallottino
- Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA), Centro di ricerca Ingegneria e Trasformazioni agroalimentari, Via della Pascolare 16, 00015 Monterotondo, Roma, Italy
| | - S R Stazi
- DIBAF, University of Tuscia, Via S.C. De Lellis snc, 01100, Viterbo, Italy.
| | - A D'Annibale
- DIBAF, University of Tuscia, Via S.C. De Lellis snc, 01100, Viterbo, Italy
| | - R Marabottini
- DIBAF, University of Tuscia, Via S.C. De Lellis snc, 01100, Viterbo, Italy
| | - E Allevato
- DIBAF, University of Tuscia, Via S.C. De Lellis snc, 01100, Viterbo, Italy
| | - F Antonucci
- Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA), Centro di ricerca Ingegneria e Trasformazioni agroalimentari, Via della Pascolare 16, 00015 Monterotondo, Roma, Italy
| | - C Costa
- Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA), Centro di ricerca Ingegneria e Trasformazioni agroalimentari, Via della Pascolare 16, 00015 Monterotondo, Roma, Italy
| | - M C Moscatelli
- DIBAF, University of Tuscia, Via S.C. De Lellis snc, 01100, Viterbo, Italy
| | - P Menesatti
- Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA), Centro di ricerca Ingegneria e Trasformazioni agroalimentari, Via della Pascolare 16, 00015 Monterotondo, Roma, Italy
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