Potassium detection in the soil is of significant importance for agricultural industry. In this paper, chemometrics methods of artificial neural networks (ANN) and partial least squares (PLS) were comparatively used to detect K in the soil with laser induced breakdown spectroscopy (LIBS). In total, 12 certified reference soils and 17 simulated soil samples with the K concentration of 0.1~3.3% were prepared. LIBS spectra at the wavelength of 723.62~808.24 nm were collected, and then analyzed with ANN and PLS method. The PLS model presented the result of R<sup>2</sup> <sub>val</sub>=0.92 and RMSEV=0.26, the ANN model presented the result of R<sup>2</sup> <sub>val</sub>=0.82 and RMSEV=0.40. ANN model showed under-fitting and the PLS model performed a better RPD than that of ANN. This demonstrated that the linear PLS model is capable to determinate K concentration in the soil using LIBS.
Objective: Chinese potato staple food strategy clearly pointed out the need to improve potato processing, while the bottleneck of this strategy is technology and equipment of selection of appropriate raw and processed potato. The purpose of this paper is to summarize the advanced raw and processed potato detection methods. Method: According to consult research literatures in the field of image recognition based potato quality detection, including the shape, weight, mechanical damage, germination, greening, black heart, scab potato etc., the development and direction of this field were summarized in this paper. Result: In order to obtain whole potato surface information, the hardware was built by the synchronous of image sensor and conveyor belt to achieve multi-angle images of a single potato. Researches on image recognition of potato shape are popular and mature, including qualitative discrimination on abnormal and sound potato, and even round and oval potato, with the recognition accuracy of more than 83%. Weight is an important indicator for potato grading, and the image classification accuracy presents more than 93%. The image recognition of potato mechanical damage focuses on qualitative identification, with the main affecting factors of damage shape and damage time. The image recognition of potato germination usually uses potato surface image and edge germination point. Both of the qualitative and quantitative detection of green potato have been researched, currently scab and blackheart image recognition need to be operated using the stable detection environment or specific device. The image recognition of processed potato mainly focuses on potato chips, slices and fries, etc. Conclusion: image recognition as a food rapid detection tool have been widely researched on the area of raw and processed potato quality analyses, its technique and equipment have the potential for commercialization in short term, to meet to the strategy demand of development potato as staple food in China.