Rapid identification of the storage age of dried tangerine peel using a hand-held near infrared spectrometer and machine learning

Zhang, Xin; Gao, Zhangming; Yang, Yinglin; Pan, Shaowei; Yin, Jianwei; Yu, Xiangyang

doi:10.1177/09670335211057232

Cited by 21 publications

(11 citation statements)

References 33 publications

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“…Zhang et al combined a handheld near infrared spectroscopy instrument with machine learning techniques to accurately and rapidly identify the storage age of tangerine peel. 8 Their proposed SNV-PCA-SVM method achieved an accuracy of 96.50% on the test dataset, confirming the effectiveness of near-infrared spectroscopy for rapid discrimination of the storage age of dried tangerine peel. 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.…”

Section: Introductionmentioning

confidence: 60%

“…To evaluate the SNV-RF method more comprehensively, the precision, recall and F1-score are calculated, as shown in Table 5. In the study by Zhang et al, 8 the samples were ground to have a higher sample homogeneity, and their proposed model demonstrated excellent generalization performance. Spectral imaging technology allows obtaining spectral and spatial information representing the entire sample without sample grinding, which is a significant advantage of spectral imaging.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 60%

Section: Resultsmentioning

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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“…The absorption region in the NIR spectroscopy curve is influenced by the stretching vibrations of various chemical groups, and the intensity of absorption is proportional to the content of these chemical groups. [36][37][38] In the spectral region of 950 nm to 1070 nm, the appearance of absorption valleys can be attributed to the third overtone C-H stretches. 39 Peanut oil composition contains a significant number of C-H groups, explaining the presence of these absorption valleys.…”

Section: Spectral Image Acquisition and Preprocessingmentioning

confidence: 99%

A hyperspectral band selection algorithm for identifying high oleic acid peanuts

Shao,

Li,

Sun

et al. 2024

Journal of Near Infrared Spectroscopy

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High oleic acid peanuts have higher oleic acid content and stronger oxidation stability than common peanuts, but their appearances are similar, which imposes difficulties for classifying. Based on this, the study aims to classify high oleic acid peanut to ensure its purity by using hyperspectral imaging technology. However, classification accuracy and efficiency are limited given the large amount of redundant information of hyperspectral images. The band iteration algorithm (BIA) is proposed to select characteristic bands by reducing the redundant information between spectral bands for the peanut classification. Hyperspectral images with 616 bands (from 400 nm to 1100 nm) of 126 high oleic acid peanuts and 126 common peanuts were collected. Then, BIA selected optimal bands as characteristic bands from adjacent bands according to the classification accuracy of each band subsets. Thirdly, three classification models, namely linear discriminant analysis (LDA), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA), were employed to compare the performance of BIA with successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. The experimental results show that BIA can effectively improve the classification ability of spectral data. The BIA-PLS-DA model had the best classification efficiency, and the accuracy of the test set reached 93.26%. For peanut individuals, only one peanut sample was misclassified with a classification error rate of 1.43%.

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“…After preprocessing, the data were used to identify the origin and age of tangerine peel using random forest, Knearest neighbor, and linear discriminant analysis. Zhang et al proposed a novel approach that combines nearinfrared spectroscopy with machine learning to identify the age of the tangerine peel [13]. Te method involves preprocessing the spectral data through Savitzky-Golay convolution smoothing, standard normal variate frst-order derivatives, and principal component analysis (PCA) to yield characteristic spectral variables.…”

Section: Related Workmentioning

confidence: 99%

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

Deng,

Li,

et al. 2024

Journal of Food Quality

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Xinhui tangerine peel has valuable medicinal value. The longer it is stored in an appropriate environment, the higher its flavonoid content, resulting in increased medicinal value. In order to correctly identify the age of the tangerine peel, previous studies have mostly used manual identification or physical and chemical analysis, which is a tedious and costly process. This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. To analyze the features of tangerine peel in different ages and evaluate the performance of CNFA module, we conducted comparative experiments using the CNFA-integrated network on the Xinhui tangerine peel dataset. The proposed algorithm is compared with related models of the proposed structure and other attention mechanisms. The experimental results showed that the proposed algorithm had an accuracy of 97.17%, precision of 96.18%, recall of 96.09%, and F1 score of 96.13% for age recognition of the tangerine peel, providing a visual solution for the intelligent development of the tangerine peel industry.

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Rapid identification of the storage age of dried tangerine peel using a hand-held near infrared spectrometer and machine learning

Cited by 21 publications

References 33 publications

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

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

A hyperspectral band selection algorithm for identifying high oleic acid peanuts

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

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