Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of<i> Colla Corii Asini</i> Adulteration

Wang, Kunlun; Wang, Biyao; Lv, Yan; Tao, Xueheng; Zhang, Xu; Tan, Mingqian

doi:10.1155/2018/3487985

Cited by 7 publications

(5 citation statements)

References 40 publications

(46 reference statements)

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“…Some sources suggest that up to 40 percent of ejiao is counterfeit [57]. Counterfeit production is a well-known problem and research on identifying genuine product is going on in China [2][3][4]62,63]. Our results assume that the totality of the production of ejiao is genuine, i.e., made from donkey hides only.…”

Section: Discussionmentioning

confidence: 96%

The Potential for New Donkey Farming Systems to Supply the Growing Demand for Hides

Bennett

Pfuderer

2020

Animals

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The demand for donkey hides for ejiao, a Traditional Chinese Medicine, has resulted in rapidly increasing prices for donkey hides and donkeys. This has put pressure on donkey populations globally and has implications for donkey welfare and the livelihoods of those who rely on donkeys as working animals. The aim of the research was to explore the feasibility of setting up new donkey farming systems to supply the rising demand for ejiao using a system dynamics model of donkey production. Results show that the size of the initial female breeding herd, reproductive performance, age of reproduction, percentage of female births and average breeding life of donkeys are key variables affecting the time to build up the donkey population to supply the demand for hides, which will be at least ten to fifteen years. The implications of this are: (i) prices for donkey hides will continue to increase, (ii) companies producing ejiao will use other ingredients, (iii) China will continue to source donkey hides from around the world, and (iv) there will be continued theft and illegal trade of donkeys and concerns for rural households reliant on donkeys for their livelihoods and adverse impacts on donkey welfare.

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Section: Discussionmentioning

confidence: 96%

The Potential for New Donkey Farming Systems to Supply the Growing Demand for Hides

Bennett

Pfuderer

2020

Animals

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“…MSC, SNV, and SG1were used to preprocess the original spectral data. Eliminating interference signals such as background noise, baseline drift, and stray light during spectral acquisition reduced the complexity and improved the interpretability of the model ( Wang et al, 2018 ). After optimizing the selection of parameters, the order of derivation in the SG1 algorithm was set to 1, the number of window points was set to 5, and the degree of the polynomial was set to 2.…”

Section: Methodsmentioning

confidence: 99%

Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

Yang

Wang

Zhang

et al. 2023

PeerJ Computer Science

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Purity is an important factor of maize seed quality that affects yield, and traditional seed purity identification methods are costly or time-consuming. To achieve rapid and accurate detection of the purity of maize seeds, a method for identifying maize seed varieties, using random subspace integrated learning and hyperspectral imaging technology, was proposed. A hyperspectral image of the maize seed endosperm was collected to obtain a spectral image cube with a wavelength range of 400∼1,000 nm. Methods, including Standard Normal Variate (SNV), multiplicative Scatter Correction (MSC), and Savitzky–Golay First Derivative (SG1) were used to preprocess raw spectral data. Iteratively retains informative variables (IRIV) and competitive adaptive reweighted sampling (CARS) were used to reduce the dimensions of the spectral data. A recognition model of maize seed varieties was established using k-nearest neighbor (KNN), support vector machine (SVM), line discrimination analysis (LDA) and decision tree (DT). Among the preprocessing methods, MSC has the best effect. Among the dimensionality reduction methods, IRIV has the best performance. Among the base classifiers, LDA had the highest precision. To improve the precision in identifying maize seed varieties, LDA was used as the base classifier to establish a random subspace ensemble learning (RSEL) model. Using MSC-IRIV-RSEL, precision increased from 0.9333 to 0.9556, and the Kappa coefficient increased from 0.9174 to 0.9457. This study shows that the method based on hyperspectral imaging technology combined with subspace ensemble learning algorithm is a new method for maize seed purity recognition.

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“…However, as the concentration of food adulteration increases, the penetration ability of spectral imaging is limited, and the accuracy of algorithm identification decreases. Therefore, ANN with nonlinear mapping was also used in the study for classification, such as adulteration classification of chocolate powder [ 90 ], nutmeg [ 65 ], red chili powder [ 69 ], colla corii asini [ 63 ], marine fishmeal [ 71 ], and cereal [ 72 ]. The results demonstrate the superiority of the nonlinear mapping model.…”

Section: Applications Of Machine Learning and Hsi In The Food Supply ...mentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration

Cited by 7 publications

References 40 publications

The Potential for New Donkey Farming Systems to Supply the Growing Demand for Hides

The Potential for New Donkey Farming Systems to Supply the Growing Demand for Hides

Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

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