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DISCRIMINATION OF INFECTED SILKWORM CHRYSALISES USING NEAR INFRARED SPECTROSCOPY COMBINED WITH MULTIVARIATE ANALYSIS DURING THE CULTIVATION OF Cordyceps militaris
Abstract
The objective of this study was to confirm whether near infrared spectroscopy could be used to discriminate the infected silkworm chrysalises. A total of 105 silkworm chrysalises – 65 infected and 40 uninfected – were collected at Beijing Shoucheng Agricultural Development Co., Ltd. Near infrared spectra were acquired at the head, chest, abdomen, and posterior belly of each silkworm chrysalis (both uninfected and infected). Three spectral pre-processing methods and four discrimination models were used to identify the uninfected and infected silkworm chrysalises. Results indicated that the PLS-DA model based on the spectra processed by multiplicative scatter correction (MSC) had the best discrimination performance (the prediction accuracy of calibration set and prediction set were 100 and 97.5%, respectively), and the head portion was the best position for the discrimination of uninfected and infected silkworm chrysalises. The overall conclusion was that the uninfected and infected silkworm chrysalises could be successfully identified by using near infrared spectroscopy technology in the cultivation of Cordyceps militaris.
Keywords
Supporting agencies: This work was supported financially by the Key-Area Research and Development Program of Guangdong Province (2019B020214005), the National Natural Science Foundation of China (31601216) and the Special Construction of Innovation Ability of Beijing Academy of Agriculture and Forestry Science (KJCX20170418). We also acknowledge the Key R&D and Promotion Project of the Henan Province (212102110207).
About the Authors
Y. Zhang
National Research Center of Intelligent Equipment for Agriculture;
College of Equipment Agricultural Engineering at Henan University of Science and Technology
China
China
Beijing, 100097;
Luoyang, 471003
X. Wang
National Research Center of Intelligent Equipment for Agriculture
China
Beijing, 100097
China
Beijing, 100097
Ch. Wang
National Research Center of Intelligent Equipment for Agriculture
China
Beijing, 100097
China
Beijing, 100097
Y. Zhou
National Research Center of Intelligent Equipment for Agriculture
China
Beijing, 100097
China
Beijing, 100097
D. Pan
National Research Center of Intelligent Equipment for Agriculture
China
Beijing, 100097
China
Beijing, 100097
B. Luo
National Research Center of Intelligent Equipment for Agriculture
China
Beijing, 100097
China
Beijing, 100097
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Review
For citations:
Zhang Y., Wang X., Wang Ch., Zhou Y., Pan D., Luo B. DISCRIMINATION OF INFECTED SILKWORM CHRYSALISES USING NEAR INFRARED SPECTROSCOPY COMBINED WITH MULTIVARIATE ANALYSIS DURING THE CULTIVATION OF Cordyceps militaris. Zhurnal Prikladnoii Spektroskopii. 2021;88(1):169(1)-169(8).
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