Jianrong Cai

Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools.

Total acid content (TAC) is an important index in assessing vinegar quality. This work attempted to determine TAC in vinegar using near infrared spectroscopy. We systematically studied variable selection and nonlinear regression in calibrating regression models. First, the efficient spectra intervals were selected by synergy interval PLS (Si-PLS); then, two nonlinear regression tools, which were extreme learning machine (ELM) and back propagation artificial neural network (BP-ANN), were attempted. Experiments showed that the model based on ELM and Si-PLS (Si-ELM) was superior to others, and the optimum results were achieved as follows: the root mean square error of prediction (RMSEP) was 0.2486 g/100mL, and the correlation coefficient (R(p)) was 0.9712 in the prediction set. This work demonstrated that the TAC in vinegar could be rapidly measured by NIR spectroscopy and Si-ELM algorithm showed its superiority in model calibration.

Automated tea quality classification by hyperspectral imaging

A hyperspectral imaging technique was attempted to classify green tea. Five grades of green tea samples were attempted. A hyperspectral imaging system was developed for data acquisition of tea samples. Principal component analysis was performed on the hyperspectral data to determine three optimal band images. Texture analysis was conducted on each optimal band image to extract characteristic variables. A support vector machine (SVM) was used to construct the classification model. The classification rates were 98% and 95% in the training and prediction sets, respectively. The SVM algorithm shows excellent performance in classification results in contrast with other pattern recognitions classifiers. Overall results show that the hyperspectral imaging technique coupled with a SVM classifier can be efficiently utilized to classify green tea.

Simultaneous measurement of total acid content and soluble salt-free solids content in Chinese vinegar using near-infrared spectroscopy.

Total acid content (TAC) and soluble salt-free solids content (SSFSC) in Chinese vinegar are 2 important indicators in the assessment of its quality. This paper shows the feasibility to determine TAC and SSFSC in Chinese vinegar by near-infrared (NIR) spectroscopy. Synergy interval partial least square (Si-PLS) algorithm was performed to calibrate the regression model. The number of PLS factors and the number of intervals were optimized simultaneously by cross-validation. The performance of the model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in the prediction set. The optimum Si-PLS model for TAC was achieved with RMSEP = 0.264 g/100 mL and R(p) = 0.9655; the optimum Si-PLS model for SSFSC was achieved with RMSEP = 1.93 g/100 mL and R(p) = 0.9302. The overall results demonstrated that NIR spectroscopy combined with Si-PLS could be utilized to determinate TAC and SSFSC in Chinese vinegar, and NIR spectroscopy has a potential to be used in vinegar industry.

Egg Freshness on-Line Estimation Using Machine Vision and Dynamic Weighing

This paper proposed a non-destructive method for online estimation of egg freshness based on machine vision and dynamic weighing. The machine vision system and the dynamic weighing system were developed for the measurement of egg external physical characteristic and weight. Digital signal processing was employed to denoise, analyze, and extract the effective feature information from the images and vibration signals. Then, the method of multiple linear regressions was used to build model for Haugh unit prediction using the parameters of long axis, minor axis, and weight. The performance of the predictive model using six variables was achieved, with R (correlation coefficient) of 0.8653 and RMSEP (root mean square error of prediction) of 3.7454 in prediction set. The speed of the system reaches four eggs per second for every measurement. Good consistence confirmed that the proposed method has a significant potential application in online estimation of the egg freshness.

Online Measurement of Content in Block Frozen Shrimp by Fusion of X-ray Imaging and Binocular Vision

A machine vision system based on the fusion of X-ray imaging and the binocular stereo vision was developed for the online estimation of net content in block frozen shrimp. Supported algorithms were specifically developed and programmed for the online system, including image acquisition, processing, controlling the whole process, and saving the classification results. The results indicated that the relationship between mean gray value of X-ray image and net content of shrimp is linearity. Meanwhile, the coefficient of linear model also can be represented by the thickness of block frozen shrimp. An online estimation model was built with mean gray value and thickness as dependent variables. Binocular stereo vision technology was also employed to acquire thickness information of samples to revise the estimation model. The performance of the predictive model using two variables was achieved, with R (correlation coefficient) of 0.9475 and root-mean-square error of prediction (RMSEP) of 22.0993 in prediction set. Good consistence confirmed that the proposed method has significant potential application in online estimation of content in block frozen shrimp.