Rapid On-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy

Pan, Shaowei; Xin, Zhang; Xu, Wangli; Yin, Jianwei; Gu, Hongyu; Yu, Xiangyang

doi:10.1016/j.saa.2022.120936

Cited by 36 publications

(22 citation statements)

References 28 publications

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“…The results clearly showed that the components of Laoxianghuang were clustered into three groups according to the fermentation years: 3–5 years, 8–10 years, and 15–20 years. This phenomenon is similar to the fermentation of grape wine ( 37 ), the aging of tea ( 38 ), and the storage of tangerine peel ( 39 ), where samples of the same fermentation or storage age shared some similarity and had the potential for clustering homogeneity. As with unfermented raw fruits ( 30 ) and other citrus ( 40 ), the VIP scores of PLS-DA were >1 for terpenes such as γ-terpinene, cymene, limonene, and terpinolene, which may be the signature characteristic compounds of such fruits.…”

Section: Discussionmentioning

confidence: 73%

SPME-GC–MS combined with chemometrics to assess the impact of fermentation time on the components, flavor, and function of Laoxianghuang

et al. 2022

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Laoxianghuang, fermented from Citrus medica L. var. Sarcodactylis Swingle of the Rutaceae family, is a medicinal food. The volatiles of Laoxianghuang fermented in different years were obtained by solid-phase microextraction combined with gas chromatography–mass spectrometry (SPME-GC–MS). Meanwhile, the evolution of its component-flavor function during the fermentation process was explored in depth by combining chemometrics and performance analyses. To extract the volatile compounds from Laoxianghuang, the fiber coating, extraction time, and desorption temperature were optimized in terms of the number and area of peaks. A polydimethylsiloxane/divinylbenzene (PDMS/DVB) with a thickness of 65 μm fiber, extraction time of 30 min, and desorption temperature of 200 °C were shown to be the optimal conditions. There were 42, 44, 52, 53, 53, and 52 volatiles identified in the 3rd, 5th, 8th, 10th, 15th, and 20th years of fermentation of Laoxianghuang, respectively. The relative contents were 97.87%, 98.50%, 98.77%, 98.85%, 99.08%, and 98.36%, respectively. Terpenes (mainly limonene, γ-terpinene and cymene) displayed the highest relative content and were positively correlated with the year of fermentation, followed by alcohols (mainly α-terpineol, β-terpinenol, and γ-terpineol), ketones (mainly cyclohexanone, D(+)-carvone and β-ionone), aldehydes (2-furaldehyde, 5-methylfurfural, and 1-nonanal), phenols (thymol, chlorothymol, and eugenol), esters (bornyl formate, citronellyl acetate, and neryl acetate), and ethers (n-octyl ether and anethole). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed a closer relationship between the composition of Laoxianghuang with similar fermentation years of the same gradient (3rd-5th, 8th-10th, and 15th-20th). Partial least squares discriminant analysis (PLS-DA) VIP scores and PCA-biplot showed that α-terpineol, γ-terpinene, cymene, and limonene were the differential candidate biomarkers. Flavor analysis revealed that Laoxianghuang exhibited wood odor from the 3rd to the 10th year of fermentation, while herb odor appeared in the 15th and the 20th year. This study analyzed the changing pattern of the flavor and function of Laoxianghuang through the evolution of the composition, which provided a theoretical basis for further research on subsequent fermentation.

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Section: Discussionmentioning

confidence: 73%

SPME-GC–MS combined with chemometrics to assess the impact of fermentation time on the components, flavor, and function of Laoxianghuang

et al. 2022

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“…The red light band (670-760 nm) and near-infrared band (761-950 nm) represent the sample feathers in a different dimension [17]. Consequently, the sensitivity band was selected in the red light band and near-infrared band based on the correlation coefficient respectively.…”

Section: Screening the Sensitivity Band Of The Hyperspectramentioning

confidence: 99%

Hyperspectral estimation of the soluble solid content of intact netted melons decomposed by continuous wavelet transform

Zhang

Shi

Wei³

et al. 2022

Front. Phys.

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Netted melons are welcomed for their soft and sweet pulp and strong aroma during the best-tasting period. The best-tasting period was highly correlated with its soluble solid content (SSC). However, the SSC of the intact melon was difficult to determine due to the low relationship between the hardness, color, or appearance of fruit peel and its SSC. Consequently, a rapid, accurate, and non-destructive method to determine the SSC of netted melons was the key to determining the best-tasting period. A hyperspectral model was constructed to estimate the SSC of intact netted melons. The combination of continuous wavelet transform and partial least squares or random forest algorithm was employed to improve the estimation accuracy of the hyperspectral model. Specifically, the hyperspectra of the diffuse reflection and SSC of 261 fruit samples were collected. The sensitivity band was screened based on the correlation analysis and continuous wavelet transform decomposition. The correlation coefficient and RMSE of the random forest regression model decomposed by the continuous wavelet transform were 0.72 and 0.98%, respectively. The decomposition of the continuous wavelet transform improved the correlation coefficient by 5 and 1.178 times at 754 and 880 nm, respectively. The random forest regression model enhanced the determination coefficient by at least 56.5% than the partial least squares regression model, and the continuous wavelet transform decomposition further enhanced the determination coefficient of the random forest regression model by 4.34%. Meanwhile, the RMSE of the random forest regression model was reduced. Therefore, the decomposition of the continuous wavelet transform improved the stability and prediction ability of the random forest regression model.

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“…Pan et al also used a handheld NIR spectrometer to collect the NIR diffuse reflectance spectrum of the surface of the Pericarpium Citri Reticulatae, and established machine learning models to achieve rapid and non-destructive detection of the origin and storage age of the Pericarpium Citri Reticulatae. 9 Spectral imaging technologies, which combine spectral information and spatial information, have been widely used in identification of soil, crop and agricultural product. Spectral imaging technology mainly includes multispectral and hyperspectral imaging.…”

Section: Introductionmentioning

confidence: 99%

“…Pan et al also used a handheld NIR spectrometer to collect the NIR diffuse reflectance spectrum of the surface of the Pericarpium Citri Reticulatae, and established machine learning models to achieve rapid and non-destructive detection of the origin and storage age of the Pericarpium Citri Reticulatae. 9 Chu et al proposed a new lightweight convolutional neural network model for identifying Pericarpium Citri Reticulatae images captured by industrial cameras, which can also accurately and non-destructive identify the storage age of Pericarpium Citri Reticulatae. 10 However, traditional NIR spectrometers can only obtain spectral information of samples, and traditional industrial cameras can only obtain spatial information.…”

Section: Introductionmentioning

confidence: 99%

On-site rapid detection of aging of Pericarpium Citri Reticulatae using multispectral imaging

Guo,

Yu,

Hong

et al. 2023

Journal of Near Infrared Spectroscopy

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Pericarpium Citri Reticulatae is a traditional Chinese medicine with high medicinal value, and its storage age has a great impact on its ethno-pharmaceutical relevance. At present, there is a situation in the market place where Pericarpium Citri Reticulatae with short storage age is fraudulently sold as Pericarpium Citri Reticulatae with long storage age, and some unaged orange peels dyed with tea are sold as Pericarpium Citri Reticulatae at a high price. In this study, a rapid, on-site method for identifying the storage age of Xinhui Pericarpium Citri Reticulatae based on spectral imaging technology was described. The image features and spectral features were extracted respectively from the surface reflection spectral images of Pericarpium Citri Reticulatae, and a machine learning model was established to identify the storage age. This study explored the classification effect of the combination of different spectral pre-processing methods and machine learning models, and finally selected the combination of standard normal variate (SNV) and random forest (RF) models, to achieve 95% accuracy on the test dataset, showing excellent generalization performance. The result shows that the spectral imaging technology can rapidly identify the storage age of Xinhui Pericarpium Citri Reticulatae in real time, which has a great application prospect in the detection of the properties of medicinal materials.

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Rapid On-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy

Cited by 36 publications

References 28 publications

SPME-GC–MS combined with chemometrics to assess the impact of fermentation time on the components, flavor, and function of Laoxianghuang

SPME-GC–MS combined with chemometrics to assess the impact of fermentation time on the components, flavor, and function of Laoxianghuang

Hyperspectral estimation of the soluble solid content of intact netted melons decomposed by continuous wavelet transform

On-site rapid detection of aging of Pericarpium Citri Reticulatae using multispectral imaging

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