Application of the spectral image recognition module to pesticide residue risk control
2021.11.25
Agricultural product prices are mainly determined by the quantity of supply and demand, taste, and appearance. In Taiwan, which is located in tropical and subtropical regions, farmers often use pesticides in the planting process to reduce plant diseases and insect pests and improve crop yields.
However, with the rise of consumers’ awareness of food safety and health, more and more attention has been paid to the pesticide residue risk in agricultural products. Compliance with good operation specifications and rational use of pesticides can effectively reduce excessive pesticide residues. However, due to many sources of agricultural products, consumers, distributors, and gatherers can only passively learn that the products fail to pass the sampling inspection standards of health units after purchases or consumption. In addition to adverse health effects, violation fines and destruction of returned goods are unbearable losses for production and marketing units. At the international level, the safety traceable agricultural product production model established by good field specifications and independent screening tools can ensure the food safety of Chinese people and enhance the brand values of agricultural products.
Therefore, Taiwan Agricultural Chemicals and Toxic Substances Research Institute (TACTRI) develops a quick screening technology for on-site real-time detection of pesticide residues in fruits and vegetables by surface-enhanced Raman spectroscopy (SERS), to greatly enhance Raman signals of pesticide molecules through the patented measurement procedure.
After combining this technology with the high-stability and high-sensitivity metallic columnar nano-structured chips developed by a Taiwanese company, its price is reduced to only 1/5 of similar international products, which shows technical and cost advantages. On this basis, Raman scattering spectra of more than 200 pesticide molecules are established, and the Raman frequency shift and peaks can be used to compare the types and content ranges of pesticide residues in fruits and vegetables, so as to meet the requirements of qualitative and semi-quantitative screening. For crops with complex substrates, such as spices, tea, strawberries, and other agricultural products with high economic values, the artificial intelligence of machine learning is introduced to improve the screening rate or effectively conduct product rating with full-spectrum identification as the decision mechanism. The atlases of qualified samples (pesticide residues are not detected or lower than the allowable limit) are used to train AI to learn, produce identification models, and calculate the outlier degree of atlases of individual samples.
Agricultural product prices are mainly determined by the quantity of supply and demand, taste, and appearance. In Taiwan, which is located in tropical and subtropical regions, farmers often use pesticides in the planting process to reduce plant diseases and insect pests and improve crop yields.
However, with the rise of consumers’ awareness of food safety and health, more and more attention has been paid to the pesticide residue risk in agricultural products. Compliance with good operation specifications and rational use of pesticides can effectively reduce excessive pesticide residues. However, due to many sources of agricultural products, consumers, distributors, and gatherers can only passively learn that the products fail to pass the sampling inspection standards of health units after purchases or consumption. In addition to adverse health effects, violation fines and destruction of returned goods are unbearable losses for production and marketing units. At the international level, the safety traceable agricultural product production model established by good field specifications and independent screening tools can ensure the food safety of Chinese people and enhance the brand values of agricultural products.
Therefore, Taiwan Agricultural Chemicals and Toxic Substances Research Institute (TACTRI) develops a quick screening technology for on-site real-time detection of pesticide residues in fruits and vegetables by surface-enhanced Raman spectroscopy (SERS), to greatly enhance Raman signals of pesticide molecules through the patented measurement procedure.
After combining this technology with the high-stability and high-sensitivity metallic columnar nano-structured chips developed by a Taiwanese company, its price is reduced to only 1/5 of similar international products, which shows technical and cost advantages. On this basis, Raman scattering spectra of more than 200 pesticide molecules are established, and the Raman frequency shift and peaks can be used to compare the types and content ranges of pesticide residues in fruits and vegetables, so as to meet the requirements of qualitative and semi-quantitative screening. For crops with complex substrates, such as spices, tea, strawberries, and other agricultural products with high economic values, the artificial intelligence of machine learning is introduced to improve the screening rate or effectively conduct product rating with full-spectrum identification as the decision mechanism. The atlases of qualified samples (pesticide residues are not detected or lower than the allowable limit) are used to train AI to learn, produce identification models, and calculate the outlier degree of atlases of individual samples.
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