It is very important to detect the protein and fat content in milk powder fast and non-destructively. Near-infrared (NIR) and mid-infrared(MIR) spectroscopy techniques have been compared and evaluated for the determination of the protein and fat content in milk powder with the use of Least-squares support vector machines (LS-SVM). LS-SVM models have been developed by using both NIR and MIR spectra. Both methods have shown good correlations between infrared transmission values and two nutrition contents. MIRS provided better prediction performance over NIRS. It is concluded that infrared spectroscopy technique can quantify of the protein and fat content in milk powder fast and nondestructively. The process is simple and easy to operate than chemistry methods. The results can be beneficial for designing a simple and non-destructive instrument with MIRS or NIRS spectral sensor for the determination of the protein fat content in milk powder.
This research aimed for development multi-spectral imaging technique for green tea categories discrimination based on texture analysis. Three key wavelengths of 550, 650 and 800 nm were implemented in a common-aperture multi-spectral charged coupled device camera, and images were acquired for 190 unique images in a four different kinds of green tea data set. An image data set consisting of 15 texture features for each image was generated based on texture analysis techniques including grey level co-occurrence method (GLCM) and texture filtering. For optimization the texture features, 5 features that weren't correlated with the category of tea were eliminated. Unsupervised cluster analysis was conducted using the optimized texture features based on principal component analysis. The cluster analysis showed that the four kinds of green tea could be separated in the first two principal components space, however there was overlapping phenomenon among the different kinds of green tea. To enhance the performance of discrimination, least squares support vector machine (LSSVM) classifier was developed based on the optimized texture features. The excellent discrimination performance for sample in prediction set was obtained with 100%, 100%, 75% and 100% for four kinds of green tea respectively. It can be concluded that texture discrimination of green tea categories based on multi-spectral image technology is feasible.
A fast measurement of pH of yogurt using Vis/NIR-spectroscopy techniques was established in order to measuring the
acidity of yogurt rapidly. 27 samples selected separately from five different brands of yogurt were measured by
Vis/NIR-spectroscopy. The pH of yogurt on positions scanned by spectrum was measured by a pH meter. The
mathematical model between pH and Vis/NIR spectral measurements was established and developed based on partial
least squares (PLS) by using Unscramble V9.2. Then 25 unknown samples from 5 different brands were predicted based
on the mathematical model. The result shows that The correlation coefficient of pH based on PLS model is more than
0.890, and standard error of calibration (SEC) is 0.037, standard error of prediction (SEP) is 0.043. Through predicting
the pH of 25 samples of yogurt from 5 different brands, the correlation coefficient between predictive value and
measured value of those samples is more than 0918. The results show the good to excellent prediction performances.
The Vis/NIR spectroscopy technique had a significant greater accuracy for determining the value of pH. It was
concluded that the VisINIRS measurement technique can be used to measure pH of yogurt fast and accurately, and a new
method for the measurement of pH of yogurt was established.
KEYWORDS: Mathematical modeling, Spectroscopy, Near infrared, Statistical modeling, Calibration, Data modeling, Chemical analysis, Principal component analysis, Statistical analysis, Reflectivity
In order to measuring the sugar content of yogurt rapidly, a fast measurement of sugar content of yogurt using
Vis/NIR-spectroscopy techniques was established. 25 samples selected separately from five different brands of yogurt
were measured by Vis/NIR-spectroscopy. The sugar content of yogurt on positions scanned by spectrum were measured
by a sugar content meter. The mathematical model between sugar content and Vis/NIR spectral measurements was
established and developed based on partial least squares (PLS). The correlation coefficient of sugar content based on
PLS model is more than 0.894, and standard error of calibration (SEC) is 0.356, standard error of prediction (SEP) is
0.389. Through predicting the sugar content quantitatively of 35 samples of yogurt from 5 different brands, the
correlation coefficient between predictive value and measured value of those samples is more than 0.934. The results
show the good to excellent prediction performance. The Vis/NIR spectroscopy technique had significantly greater
accuracy for determining the sugar content. It was concluded that the Vis/NIRS measurement technique seems reliable to
assess the fast measurement of sugar content of yogurt, and a new method for the measurement of sugar content of
yogurt was established.
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