便携式生鲜猪肉多品质参数同时检测装置研发

Development of a portable device for simultaneous detection on multi-quality attributes of fresh pork

针对农畜产品检测现场的需求,基于可见/近红外光谱检测技术和嵌入式系统,开发了灵活方便的猪肉品质无损检测装置。该装置利用卤素灯作为光源,新型光导探头和微型光谱仪采集肉样光谱信息,通过ARM（advanced RISC machines）控制处理器进行集中控制和数据的处理;在内嵌linux操作系统上,采用Qt开发工具,设计出人性化的交互界面,并将猪肉品质的检测结果输出到装置触摸屏上。为了建立多品质无损检测数学模型,获取了猪肉里脊在400~1 000 nm波长范围内的光谱数据,通过国标方法测得猪肉里脊主要品质参数颜色（L＊、a＊、b＊）和p H值,采用标准正态变量变换（standard normalized variate,SNV）和Savitzky-Golay（S-G）平滑对光谱数据进行预处理,并结合理化数据建立偏最小二乘（partial least squares regression,PLSR）模型。用全交叉验证法选取PLSR建模的主成分数。p H值、L＊、a＊和b＊的预测相关系数为0.88、0.90、0.97和0.97,预测标准差为0.19、1.77、1.17和0.63。通过现场试验表明,轻便式多品质无损检测装置具有较高的检测精度,满足于猪肉的颜色和p H值等品质参数检测的要求。

For detecting the quality of pork, traditional optical equipment has high accuracy, whereas heavy weight, large size and high price make it difficult to use widely. The purpose of this research was to develop a portable optical device for detecting pork quality based on visible/near infrared spectroscopy and embedded system. This paper mainly explained the models building and the development of application software. Firstly, a compact and flexible system was made. Halogen lamp is as light source. To adapt to various complex environments, its hand-held probe can form black room on the surface of pork. Micro spectrometer（USB4000） receives and measures reflected light. ARM（advanced RISC machines） processor controls all parts in device and analyzes spectrum data. Based on Linux embedded operation system, liquid crystal display（LCD） touch screen interfaces with users. The whole weight of 3.5 kg makes it convenient for users. Secondly, collect the spectrum reflected from pork samples and build the partial least squares regression（PLSR） model. Before these, spectrometer parameters should be set, so that it works under the best conditions. Integration time of USB4000 was set to 7 ms, pixel boxcar width zero. Thus the reflection intensity of standard white plate was about 80% of spectrometer scale span. During experiment, after acquiring white and black spectrum data, detection probe was put on the surface of pork samples. Spectrum data in the wavelength range from 400 to 1 000 nm were collected from the surfaces of 39 pork samples, 29 spectra of which were as calibration, while others as validation. The acquired spectrum data were then processed by standard normalized variables（SNV） and Savitzky-Golay filter（S-G） to eliminate the spectra noise. After collecting the spectrum data, reference p H values of pork samples were immediately tested by p H meter（METTLER TOLEDO FE20, Switzerland）, and color parameters（L＊, a＊, b＊） were measured by precision colorimeter（HP-200, Shanghai, China）. The partial least squares regression（PLSR） was applied to establish the prediction models. Experiment results showed that prediction correlation coefficients of p H value, L＊, a＊ and b＊ were 0.94, 0.98, 0.95 and 0.85, and standard deviations of p H value, L＊, a＊ and b＊ were 0.17, 1.19, 0.42 and 0.61, respectively. Thirdly, application software was designed and developed for detecting the quality of pork. It consisted of spectrometer control unit, spectrum data acquisition unit, spectrum analysis unit, and displaying and saving unit for prediction result of pork quality. Particularly, in spectrometer control unit, all parameters of USB4000 were set as the same as those when building the PLSR models. The coefficients matrixes of models were loaded into pork quality detection software in spectrum analysis unit. After debugged, the application program detecting the quality of pork was cross-compiled, and downloaded into the device. Finally, the accuracy of models were tested. The reflect spectra of external 41 pork samples were collected and analyzed with the device. At the same time, the real values of these samples＇ p H, L＊, a＊ and b＊ were measured. For the p H value, the prediction model could give satisfactory results with the correlation coefficient（Rv） of 0.88 and the standard error of prediction（SEP） of 0.19. For the color L＊, a＊ and b＊, the prediction models could gain prediction results with the Rv of 0.90, 0.97 and 0.97, and the SEP of 1.77, 1.17 and 0.63, respectively. In conclusion, the field application results indicate that this portable device can satisfy the requirements of meat quality detection with high accuracy and good performance.