CAJ | 학술논문

目的：采用可见/近红外光谱技术,结合岭回归偏最小二乘对猪肉新鲜度进行定量分析。方法利用自行搭建的可见/近红外光谱检测系统,采集62个猪肉样品表面380~900 nm范围内的反射光谱数据,进行标准正态变量变换(standard normal variable transform, SNVT)预处理后,建立偏最小二乘(partial least square regression, PLSR)模型。利用模拟退火算法(simulated annealing, SA)和粒子群算法(particle swarm optimization, PSO)进行岭参数寻优,建立猪肉挥发性盐基氮(total volatile basic nitrogen, TVB-N)的岭回归模型。结果所建模型的相关系数和误差分别为0.9819、1.2785 mg/100 g和0.9781、1.4628 mg/100 g。结论所建立的模型取得了较好的结果,利用岭回归偏最小二乘实现了对最小二乘估计的改良,更加验证了可见近红外光谱技术对猪肉新鲜度进行定量分析的巨大应用潜力。
목적：채용가견/근홍외광보기술,결합령회귀편최소이승대저육신선도진행정량분석。방법이용자행탑건적가견/근홍외광보검측계통,채집62개저육양품표면380~900 nm범위내적반사광보수거,진행표준정태변량변환(standard normal variable transform, SNVT)예처리후,건립편최소이승(partial least square regression, PLSR)모형。이용모의퇴화산법(simulated annealing, SA)화입자군산법(particle swarm optimization, PSO)진행령삼수심우,건립저육휘발성염기담(total volatile basic nitrogen, TVB-N)적령회귀모형。결과소건모형적상관계수화오차분별위0.9819、1.2785 mg/100 g화0.9781、1.4628 mg/100 g。결론소건립적모형취득료교호적결과,이용령회귀편최소이승실현료대최소이승고계적개량,경가험증료가견근홍외광보기술대저육신선도진행정량분석적거대응용잠력。
Objective To quantitatively evaluate pork freshness based on visible-near infrared spectroscopy combined with ridge regression. Methods A laboratory visible/near-infrared spectroscopy system was built to collect reflectance spectra in the 380~900 nm in 62 pork samples and the ridge regression was employed to establish partial least squares regression (PLSR) models. The reflectance spectra of samples were performed with pretreatment method of standard normal variable transform (SNVT), and then PLSR model was built. Simulated annealing (SA) algorithm and particle swarm optimization (PSO) algorithm were employed to optimize ridge parameter k. Then the ridge regression model for total volatile basic nitrogen (TVB-N) value was established. Results The correlation coefficients and errors were 0.9819 and 1.2785mg/100 g, and 0.9781 and 1.4628 mg/100 g, respectively. Conclusion The models built had achieved good results, and the ridge regression using partial least squares could improve the model performance of traditional partial least squares models. It verified the huge potential application of visible near infrared spectroscopy to quantitatively analyze pork freshness