[Study on predicting total acid content and soluble sugar of tomato juice by near infrared optical fiber spectrometer technique].

In order to explore a simple, rapid and efficient tomato quality detection method, in the present experiment near infrared spectroscopy and optical fiber sensing technology were applied to quickly measure the nutrition ingredient content in tomato juice samples. The main instrument used in this experiment was near infrared optical fiber spectrometer in a wavelength range from 900 to 2 500 nm, which measured the absorbance of the tomato juice samples; A collection of one hundred and sixty-four tomato juice samples were selected as the standard samples, the spectra and the corresponding chemical value were measured. Partial least squares (PLS) was adopted to establish the mathematical model of the total acid and soluble sugar content in tomato juice samples, and the regression equation was statistically analysed. The total acid in tomato juice prediction correlation coefficient was 0.967, calibration standard deviation (RMSEC) was 0.133, standard error of prediction (RMSEP) was 0.103; the soluble sugar prediction correlation coefficient is 0.976, calibration standard deviation (RMSEC) was 0.463, and the standard error of prediction (RMSEP) was 0. 460. The above data achieved better forecasting results, which showed that the method of quantitative analysis of tomato fruit multicomponent content was feasible. The method is rapid, simple and can do multicomponent analysis on the same sample simultaneously. It is a promising sensor and gradually becoming a international research focus in sensor field.

[Rapid discriminating hogwash oil and edible vegetable oil using near infrared optical fiber spectrometer technique].

In the present study, a new method using near infrared spectroscopy combined with optical fiber sensing technology was applied to the analysis of hogwash oil in blended oil. The 50 samples were a blend of frying oil and "nine three" soybean oil according to a certain volume ratio. The near infrared transmission spectroscopies were collected and the quantitative analysis model of frying oil was established by partial least squares (PLS) and BP artificial neural network The coefficients of determina- tion of calibration sets were 0.908 and 0.934 respectively. The coefficients of determination of validation sets were 0.961 and 0.952, the root mean square error of calibrations (RMSEC) was 0.184 and 0.136, and the root mean square error of predictions (RMSEP) was all 0.111 6. They conform to the model application requirement. At the same time, frying oil and qualified edible oil were identified with the principal component analysis (PCA), and the accurate rate was 100%. The experiment proved that near infrared spectral technology not only can quickly and accurately identify hogwash oil, but also can quantitatively detect hog- wash oil. This method has a wide application prospect in the detection of oil.