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Wei W.,China Agricultural University | Wei W.,National nter for Agro processing Equipment | Peng Y.,China Agricultural University | Peng Y.,National nter for Agro processing Equipment
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2016

A hand-held and portable device for detecting meat quality parameters was designed, which was composed of hardware components including detecting probe module with multispectral light sources array, constant flow driver module, multispectral data acquisition and processing module, wireless transmission and display module, as well as application software based on Android system on terminal display platform. In addition, multispectral array contained 64 LED light sources which related to the quality parameters and it were divided into eight groups and each group incorporated eight different LED light sources of which the center wavelengths were 475, 515, 525, 575, 610, 760, 810, 910 nm respectively, forming a diameter of 5 cm brightness uniformity detection range for obtaining spectral data of meat samples. The dimension of this device was 175 mm(L)×115 mm(W)×25 mm(H) and the weight was about 0.45 kg. In addition, cost of device was less 2500 RMB. When using multispectral light array to detect samples, some lights were absorbed by samples and another part was reflected from samples which was called diffuse light. After obtaining diffuse light from samples, a silicon photodiode detector with spectral response range of 400~1100 nm was installed to receive diffuse light from pork meat in detection zone, and then the signal from silicon photodiode detector were amplified and processed by amplifier chip and A/D converter chip, then different wavelengths spectral data were calculated for establishing meat quality prediction model. For verifying this device, 43 pork samples with different quality attributes were collected for data acquisition and three algorithms including multiple linear regression (MLR), partial least square regression (PLSR) and multiple linear regression (MLR) mathematical with stepwise method were employed to establish pork prediction models. The parameters included color parameters (L*, a*, b*) and total volatile basic nitrogen (TVB-N) content in meat respectively. The 43 samples were divided into calibration and validation sets according to the proportion of 3:1 to achieve more reasonable prediction results. The results showed that the multiple linear regression (MLR) mathematical with stepwise method had the best prediction model, the correlation coefficients of prediction (Rp) for pork color (L*, a*, b*) and TVB-N content were 0.9471, 0.8504, 0.8563, 0.8027, respectively. At last, the same number of 12 pork samples from two groups of A, B at different time periods were used to verify the feasibility of this model, and the coefficient between predicted values of pork color (L*, a*, b*) and TVB-N content and their true values in each group were more than 0.80. In addition, coefficient of variation (CV) of verifying results in two groups was less than 1.1%, which indicated that this model of this device had certain stability to some extent. This experiment demonstrated that it has the potential in nondestructive detection for assessing meat freshness using this device, which not only meet the requirements of nondestructive testing but also can be widely applied for assessing meat quality in future. © 2016, Chinese Society of Agricultural Machinery. All right reserved.

Shi L.,China Agricultural University | Shi L.,National nter for Agro Processing Equipment | Guo H.,China Agricultural University | Guo H.,Xinjiang Agricultural University | And 6 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2015

Moisture content is considered as an important evaluation index for beef quality. This study focused on development of nondestructive rapid detection system device for assessing beef quality based on VIS/NIR spectroscopy. Working principal and process of the system, hardware composition, and software were introduced. Hardware of detecting system included two spectrometers, handheld probe, control instrument, bifurcated optical fiber, power unit and container. Two spectrometers in the spectral region of 400~960 nm and 900~2600 nm were coupled together for spectral acquisition. The wavelength range of visible and near infrared spectroscopy was covered and the characteristic wavelengths in VIS/NIR band of moisture, protein, fat and other main components of beef were detected by the two spectrometers. Another important part was probe. The handheld probe was designed with two optical channels in order to reduce the effect of light source on fiber probe. One channel was designed with angle of 45° for light source and the other one was designed to be vertical for the fiber probe. Inner surface of the two channels was covered by white barium sulfate to make the light distribution even. The handheld probe was designed to ensure the same distance between the end of fiber probe and the surface of sample. Experiment showed that different samples could be detected by adjusting distance between the end of fiber probe and the bottom surface of handheld probe. One end of the bifurcated fiber was connected with handheld probe and the other two were connected with two spectrometers. Software of spectral data collecting and rapid detection was developed by using VC++, and can be run in Windows environment. Main modules of the software included parameter setting module, sample information management module, trigger control module, spectral information acquisition module, quality evaluation module and results display and storage module. The system could collect spectral data, process the data, detect the quality of sample and display the results. First, the system was used to acquire optical data from 57 beef samples of M. longissimus dorsi, and build the prediction model of beef moisture content by visible spectra, NIR spectra, full spectra, respectively. The results showed that the prediction model developed by full spectra had the highest accuracy. The correlation coefficient of calibration (Rc) and the correlation coefficient of prediction (Rp) of the prediction model were 0.96 and 0.88, respectively. Then, experiment on the system was done to detect moisture content of beef on the processing line in two beef slaughtering enterprises. In this test, 84 beef ridge samples were extracted for moisture content detection. The detection results from the experiment yielded satisfactory results. Correct rate of non-destructive rapid detection system device testing on moisture content was 92.8%. The result shows that the system can be used for nondestructive rapid detection of beef quality with high accuracy and reliability as well as repeatability. ©, 2015, Chinese Society of Agricultural Machinery. All right reserved.

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