Rapid Detection of Moisture Content in the Processing of Longjing Tea by Micro-Near-Infrared Spectroscopy and a Portable Colorimeter Based on a Data Fusion Strategy

Zong, Xuyan; Sheng, Xufeng; Li, Li; Zan, Jiezhong; Jiang, Yongwen; Zou, Hanting; Shen, Shuai; Yuan, Haibo

doi:10.3390/horticulturae8111007

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…The control of water content during the processing of tea can be useful to stabilize the quality and flavor of the beverage. The results obtained demonstrate that the data fusion of a micro-NIR spectrometer and portable colorimeter is feasible to establish a quantitative prediction model of the moisture content in Longjing tea [116].…”

Section: Teamentioning

confidence: 84%

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

Basile,

Mallardi,

Cardone

2023

Chemosensors

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In recent years, there has been a significant rise in the popularity of plant-based products due to various reasons, such as ethical concerns, environmental sustainability, and health benefits. Sensory analysis is a powerful tool for evaluating the human appreciation of food and drink products. To link the sensory evaluation to the chemical and textural compositions, further quantitative analyses are required. Unfortunately, due to the destructive nature of sensory analysis techniques, quantitative evaluation can only be performed on samples that are different from those ingested. The quantitative knowledge of the analytical parameters of the exact sample ingested would be far more informative. Coupling non-destructive techniques, such as near-infrared (NIR) and hyperspectral imaging (HSI) spectroscopy, to sensory evaluation presents several advantages. The intact sample can be analyzed before ingestion, providing in a short amount of time matrices of quantitative data of several parameters at once. In this review, NIR and imaging-based techniques coupled with chemometrics based on artificial intelligence and machine learning for sensory evaluation are documented. To date, no review article covering the application of these non-destructive techniques to sensory analysis following a reproducible protocol has been published. This paper provides an objective and comprehensive overview of the current applications of spectroscopic and sensory analyses based on the state-of-the-art literature from 2000 to 2023.

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Section: Teamentioning

confidence: 84%

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

Basile,

Mallardi,

Cardone

2023

Chemosensors

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“…In addition, there was also a significant difference between the HAD-and LTD-treated tea; the b* value of the HAD-treated tea was significantly higher than that of the LTD-treated tea, but its a* value was significantly lower than that of the LTD-treated tea, so the HAD treatment led to better retention of the yellow appearance of the partridge tea, while the LTD treatment led to better retention of the red appearance of the partridge tea. [36,37]. Data are presented as mean ± standard deviation (n = 6).…”

Section: Appearance Of Partridge Tea Under Different Drying Methodsmentioning

confidence: 99%

“…The lower temperature could better preserve the original color of the samples to achieve better preservation [12]. Note: L* denotes luminance; a* denotes poor red-green, b* denotes poor yellow-blue [36,37]. Data are presented as mean ± standard deviation (n = 6).…”

Section: Classification Of Volatile Compounds In Partridge Teamentioning

confidence: 99%

Aroma Difference Analysis of Partridge Tea (Mallotus oblongifolius) with Different Drying Treatments Based on HS-SPME-GC-MS Technique

Gui,

Feng,

Tang

et al. 2023

Molecules

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Partridge tea has high medicinal value due to its rich content of terpenoids, phenols, flavonoids, and other related bioactive components. In order to study the best drying method for partridge tea, four treatments, including outdoor sun drying (OD), indoor shade drying (ID), hot-air drying (HAD), and low-temperature freeze-drying (LTD), were performed. The results showed that the OD and HAD treatments favored the retention of the red color of their products, while the ID and LTD treatments were more favorable for the retention of the green color. The HS-SPME-GC-MS results showed that a total of 82 compounds were identified in the four drying treatments of partridge tea, and the most abundant compounds were terpenoids (88.34–89.92%). The HAD-treated tea had the highest terpenoid content (89.92%) and high levels of flavor compounds typical of partridge tea (52.28%). OPLS-DA and PCA showed that α-copaene, β-bourbonene, caryophyllene, α-guaiene, and δ-cadinene could be considered candidate marker compounds for judging the aroma quality of partridge tea with different drying treatments. This study will not only provide a basis for processing and flavor quality control but also for spice and seasoning product development in partridge tea.

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Detection of moisture content of edamame based on the fusion of reflectance and transmittance spectra of hyperspectral imaging

Li,

Su,

Yin

et al. 2024

Journal of Chemometrics

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Edamame is a nutritious and economically valuable soybean. The moisture content is an important indicator of the quality of the edamame. The traditional methods in the detection of moisture content of edamame have the disadvantage of large detection errors. In this research, the fusion of transmittance and reflectance spectra of hyperspectral imaging combined with chemometrics was proposed to predict the moisture content of edamame. Also, the effect of different preprocessing of the spectra on the predictive performance was analyzed. Single spectra, primary fusion spectra, and intermediate fusion spectra were established as the prediction models for partial least squares regression (PLSR) and partial least squares support vector regression (LSSVR), respectively. The results of the prediction models showed that the spectral transform absorption (STA) combined with PLSR has the best prediction performance for a single spectrum with predictive correlation (RP) of 0.7749 and ratio of prediction to deviation (RPD) of 1.7. Standard normal variate (SNV) combined with LSSVR has the best prediction performance for primary fusion spectra with RP of 0.8821 and RPD of 1.9. SNV combined with LSSVR has the best prediction performance for intermediate fusion spectra with RP of 0.9149 and RPD of 2.4. The Rp and RPD of prediction models of the moisture content of edamame based on fusion spectra were significantly improved compared with single spectra. Compared with primary fusion, intermediate fusion is a more suitable fusion strategy. This research provides experimental basis for the prediction of moisture content of edamame using spectral fusion combined with chemometrics.

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Rapid Detection of Moisture Content in the Processing of Longjing Tea by Micro-Near-Infrared Spectroscopy and a Portable Colorimeter Based on a Data Fusion Strategy

Cited by 7 publications

References 33 publications

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

Spectroscopy, a Tool for the Non-Destructive Sensory Analysis of Plant-Based Foods and Beverages: A Comprehensive Review

Aroma Difference Analysis of Partridge Tea (Mallotus oblongifolius) with Different Drying Treatments Based on HS-SPME-GC-MS Technique

Detection of moisture content of edamame based on the fusion of reflectance and transmittance spectra of hyperspectral imaging

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