Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology

Ouyang, Qin; Wang, Li; Park, Bosoon; Kang, Rui; Chen, Quansheng

doi:10.1016/j.foodchem.2021.129141

Cited by 38 publications

(9 citation statements)

References 35 publications

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“…Meanwhile, the effective wavelengths of TF were almost consistent with former published studies, where 1,100 to 1,140,nm and 1,650 to 1,690 nm corresponded to the first overtone region and second overtone region of –CH 3 from flavonoids, respectively ( Figure 3C ) ( 34 ). Also, the 1,430 to 1,450 nm corresponded to the second overtone region of –CH from polyphenols ( 36 ), and wavelengths intervals of 425–520 and 725–995 nm corresponded to the most abundant phenolic compounds of ferulic acid in RRB ( Figure 3D ) ( 37 ).…”

Section: Discussionmentioning

confidence: 99%

Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics

Wang

Zhang²,

Yuan³

et al. 2022

Front. Nutr.

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The geographical origin and the important nutrient contents greatly affect the quality of red raspberry (RRB, Rubus idaeus L.), a popular fruit with various health benefits. In this study, a chemometrics-assisted hyperspectral imaging (HSI) method was developed for predicting the nutrient contents, including pectin polysaccharides (PPS), reducing sugars (RS), total flavonoids (TF) and total phenolics (TP), and identifying the geographical origin of RRB fruits. The results showed that these nutrient contents in RRB fruits had significant differences between regions (P < 0.05) and could be well predicted based on the HSI full or effective wavelengths selected through competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA). The best prediction results of PPS, RS, TF, and TP contents were achieved with the highest residual predictive deviation (RPD) values of 3.66, 3.95, 2.85, and 4.85, respectively. The RRB fruits from multi-regions in China were effectively distinguished by using the first derivative-partial least squares discriminant analysis (DER-PLSDA) model, with an accuracy of above 97%. Meanwhile, the fruits from three protected geographical indication (PGI) regions were successfully classified by using the orthogonal partial least squares discrimination analysis (OPLSDA) model, with an accuracy of above 98%. The study results indicate that HSI assisted with chemometrics is a promising method for predicting the important nutrient contents and identifying the geographical origin of red raspberry fruits.

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Section: Discussionmentioning

confidence: 99%

Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics

Wang

Zhang²,

Yuan³

et al. 2022

Front. Nutr.

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“…The pre-processed spectral data exhibited a multicollinearity problem, so it was necessary to find the feature variables beneficial to the prediction results and eliminate the invalid variables. In this study, the Bootstrapping soft shrinkage (Boss) algorithm (Deng et al, 2016;Ouyang et al, 2021), the competitive adaptive reweighted sampling (CARS) algorithm (Zhang et al, 2019;Shicheng et al, 2021), the iteratively variable subset optimization (IVSO) algorithm (Sun et al, 2021), the Interval Variable Iterative Space Shrinkage Approach (IVISSA) (Cheng et al, 2020;Hao et al, 2022) and the Model adaptive space shrinkage (MASS) (Wen et al, 2016) methods were used to extract the spectral data. The overall equipment structure: (A) Gaia hyperspectral sorter; (B) Gaia fluorescence spectral detection system.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

Chen

Wang

et al. 2023

Front. Plant Sci.

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The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the Rp2, Rc2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the Rp2, Rc2, and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

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“…[1][2][3] It can not only determine the object's existence but also provide information about its composition. 4 Spectral technologies have been widely used in food safety, [4][5][6] biomedicine, [7][8][9] and remote sensing. 10,11 Various spectral systems have been designed and manufactured.…”

Section: Introductionmentioning

confidence: 99%

Computational hyperspectral devices based on quasi-random metasurface supercells

Chen

Yang

et al. 2023

Nanoscale

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Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology

Cited by 38 publications

References 35 publications

Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics

Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

Computational hyperspectral devices based on quasi-random metasurface supercells

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