The potential of hyperspectral imaging system was evaluated for the rapid identification of rice origin. 240 samples from four different regions of China were imaged by a hyperspectral imaging system. Hyperspectral images were studied from the three principal aspects (spectral, morphological and texture features). Support vector machine was used for developing the identification models. Seven models based on spectral, morphological, texture, combined spectral and morphological, combined spectral and texture, combined morphological and texture and combined spectral, morphological and texture features were developed for seeking the optimal feature combination. Nine important wavelengths were determined by principal component analysis. The results showed that the highest accuracy (91.67%) was obtained from combined spectral, morphological and texture features. This study demonstrated that hyperspectral imaging could provide a rapid identification of rice origin and the method of feature combination could be very helpful to improve the performance of identification models. Practical Applications The price and quality of rice mainly depends on its geographical origin in the food market. Traditional methods for identification of rice origin mainly focus on the appearance of rice and depend on the feelings of professionals, which are tedious, time‐consuming, expensive and greatly influenced by subjective factors. Therefore, the results in our paper can provide a foundational basis to develop a real‐time inspection system for the rapid, precise and non‐destructive identification of rice origin.

In this study, a method for quantitative determination of rice moisture based on hyperspectral imaging technology was proposed. First, the hyperspectral imaging system in the spectral range of 871-1766 nm was used to collect the hyperspectral images of 120 rice samples of 10 moisture grades. Support vector regression (SVR), least-squares support vector regression (LS-SVR), and bacterial colony chemotaxis least-squares support vector regression (BCC-LS-SVR) models were established to determine the moisture content by using full wavelengths spectra data. Among all the models, the BCC-LS-SVR model showed the best results. To simplify the calibration model, successive projections algorithm (SPA) was used for feature selection and the number of characteristic wavelengths was determined as 25. Principal component analysis (PCA) was used for feature extraction and the cumulative contribution rate of the first six principal components reached 99%, which could reflect most of the information of the full spectra data. Three new regression models based on the selected wavelengths were built and the results were improved obviously. The BCC-LS-SVR-SPA model got the best accuracy in prediction and calibration with R 2 p of 0.980, RMSEP of 0.967%, R 2 c of 0.985 and RMSEC of 0.591%. The overall results from this study demonstrated that hyperspectral image technology is feasible to detect rice moisture. PRACTICAL APPLICATIONSThe quality of rice has a direct relationship with the moisture content of rice. Because of the moisture content over standard, rice storage time becomes shorter and rice is easy to go bad. It's harmful to eat this rice for a long time. Traditional methods for identification of rice moisture mainly focus on the appearance of rice and depend on the feelings of professionals, which are tedious, time-consuming, expensive and greatly influenced by subjective factors. Hyperspectral imaging technology has the advantages of nondestructive, rapid, non-pollution, and so on. The results showed that hyperspectral imaging technology for the detection of the rice moisture is feasible and it can measure the moisture of rice.

In this study, a method for the quantitative determination of rice starch based on hyperspectral imaging technology was proposed. First, the hyperspectral imaging system in the spectral range of 871-1766 nm was used to collect the hyperspectral images of 100 rice samples of 10 starch grades. The support vector regression (SVR) model was established to determine the starch content by using full-wavelength spectra data. Among all the models, the SVR-principal component analysis (SVR-PCA) model with the Radial Basis Function showed the best results. To simplify the calibration model, PCA was used for feature extraction and the cumulative contribution rate of the first six principal components reached 99%, which could reflect most of the information of the full spectra data. Three new regression models based on the selected wavelengths were developed and the results were improved obviously. The SVR-PCA model obtained the best accuracy in prediction and calibration with the determination coefficients of prediction (R 2 p) of 0.991, root mean square error of prediction (RMSEP) of 0.669%, the determination coefficients of calibration (R 2 c) of 0.989, and root mean square error of calibration (RMSEC) of 0.445%. The overall results from this study demonstrated that the hyperspectral image technology is feasible to detect rice starch.

Abstract. Fast and accurate discrimination of pesticide residue in mulberry leaf is very important for sericulture and silk textile industry. Therefore, a hyperspectral imaging approach with the spectral range of 390-1050 nm was used for the discrimination of pesticide residue in mulberry leaf. 120 mulberry leaves samples including 60 samples without pesticide residue and 60 samples with pesticide residue were imaged by the VIS-NIR hyperspectral imaging system. ENVI software was used to explore the region of interest and extract the corresponding spectral data. Partial least square discriminant analysis (PLSDA) was used to establish the discriminative model for the discrimination of pesticide residue in mulberry leaf and the model achieved 98.33% calibration accuracy and 93.33% prediction accuracy. A total of 9 important wavelengths were selected according to the regression coefficient in the PLSDA model. A simplified PLSDA model was developed based on the important wavelengths and it achieved similar results (96.67% and 93.33%). The results showed that the model based on the selected important wavelengths was comparable to the model based on the full wavelengths, and it was feasible to use hyperspectral imaging technology for discrimination of pesticide residue in mulberry leaf.

This paper examines the relationship between climate change and inequality, evaluates three existing approaches from both macro principles and micro practices, and proposes the potential improvements for those approaches. Available evidence indicates that climate change exacerbates inequality globally and the existing approaches are insufficient and still need to be more aggressive. More specifically, the principle of Common but Differentiated Responsibilities and Respective Capabilities (CBDR-RC) in the United Nations Framework Convention on Climate Change (UNFCCC) is blunt to effectively address climate change and respond to inequality even by distributing the common responsibilities differently to the individual countries. Developed countries should take the responsibility to finance climate change due to the principle “the polluter pays” and the obligation to protect human rights; however, developed countries have not yet met their climate finance obligations. Similarly, the international carbon market has been viewed as a feasible measure, while additional actions are still needed to respond to the inequalities exacerbated by climate change.