CAJ | 학술논문

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pH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.pH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pH值等品质参数检测的要求.
침대농축산품검측현장적수구,기우가견/근홍외광보검측기술화감입식계통,개발료령활방편적저육품질무손검측장치.해장치이용서소등작위광원,신형광도탐두화미형광보의채집육양광보신식,통과ARM(advanced RISC machines)공제처리기진행집중공제화수거적처리;재내감linux조작계통상,채용Qt개발공구,설계출인성화적교호계면,병장저육품질적검측결과수출도장치촉모병상.위료건립다품질무손검측수학모형,획취료저육리척재 400~1 000 nm파장범위내적광보수거,통과국표방법측득저육리척주요품질삼수안색(L*、a*、b*)화pH치,채용표준정태변량변환(standard normalized variate, SNV)화Savitzky-Golay(S-G)평활대광보수거진행예처리,병결합이화수거건립편최소이승(partial least squares regression, PLSR)모형.용전교차험증법선취PLSR건모적주성분수.pH치、L*、a*화b*적예측상관계수위0.88、0.90、0.97화0.97,예측표준차위0.19、1.77、1.17화0.63.통과현장시험표명,경편식다품질무손검측장치구유교고적검측정도,만족우저육적안색화pH치등품질삼수검측적요구.
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 pH values of pork samples were immediately tested by pH 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 pH value, L*, a* and b* were 0.94, 0.98, 0.95 and 0.85, and standard deviations of pH 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' pH, L*, a* and b* were measured. For the pH 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.