本文關(guān)鍵詞： 豬肉新鮮度 激光散斑 散斑活性 慣性矩 互相關(guān)系數(shù) LDA線性判別分析　出處：《江蘇大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：冷鮮豬肉是人們?nèi)粘Ｉ钪饕娜忸愊M(fèi)品種。由于食品安全事件頻頻發(fā)生,人們對(duì)豬肉品質(zhì)日趨重視。傳統(tǒng)的豬肉新鮮度檢測(cè)方法主要為感官評(píng)定和實(shí)驗(yàn)室理化檢測(cè),前者檢測(cè)結(jié)果不穩(wěn)定,而后者檢測(cè)過程耗時(shí)費(fèi)力,且不適應(yīng)現(xiàn)場(chǎng)快速檢測(cè),難以滿足消費(fèi)者的需求。而激光散斑技術(shù)具有快速、無損、簡(jiǎn)便、成本低等優(yōu)勢(shì)。本研究將激光散斑技術(shù)應(yīng)用于冷鮮豬肉新鮮度的檢測(cè),具體研究?jī)?nèi)容如下:1、基于國內(nèi)外學(xué)者對(duì)激光散斑技術(shù)的研究結(jié)果,確定了冷鮮豬肉新鮮度激光散斑檢測(cè)的整體試驗(yàn)方案,設(shè)計(jì)并搭建了激光散斑試驗(yàn)裝置,編寫了基于VS2013+MFC開發(fā)的散斑圖像批量采集及處理程序,并通過預(yù)實(shí)驗(yàn)確定了最佳試驗(yàn)參數(shù),其中激光波長為465 nm和660 nm、激光功率為15.7 mW、采集時(shí)間為60 ms、激光入射角為30°。2、針對(duì)散斑圖像處理過程的研究發(fā)現(xiàn),散斑圖像不同行的選取會(huì)對(duì)IM值產(chǎn)生影響,且傳統(tǒng)IM算法受異常值干擾較大,為此提出三點(diǎn)改進(jìn):設(shè)計(jì)了排序算法,動(dòng)態(tài)選擇散斑活性最高峰及周圍2個(gè)相鄰行,依此計(jì)算樣本IM值;改進(jìn)共生矩陣的修正矩陣計(jì)算方法;改進(jìn)非零元素偏離對(duì)角線距離的計(jì)算方法。結(jié)果顯示,排序算法能快速定位IM最高峰位置,且改進(jìn)后的方法可以有效抑制異常值干擾,更能真實(shí)反映出樣本的活性差異。3、分別對(duì)冷藏期間豬肉失水量、肉色(L*、a*、b*)及TVB-N含量變化與慣性矩IM和互相關(guān)系數(shù)Ckτ值進(jìn)行分析。發(fā)現(xiàn)豬肉失水量變化較小(0.14g/d)時(shí),對(duì)樣本散斑活性變化影響便較小,而失水量變化較多(2g/d)時(shí),水分散失導(dǎo)致樣本散斑活性明顯降低,這說明水分是影響散斑活性變化的主要原因。豬肉表面顏色與散斑活性指標(biāo)均呈正相關(guān)關(guān)系,其中a*值與散斑活性指標(biāo)間的相關(guān)性最大(0.8),說明散斑活性能反映出豬肉的新鮮度變化。豬肉冷藏期間的TVB-N含量與散斑活性指標(biāo)呈負(fù)相關(guān)關(guān)系,因此僅依靠散斑活性無法對(duì)TVB-N含量做出準(zhǔn)確預(yù)測(cè)。4、選取465 nm和660 nm兩種波長,基于IM和Ckτ值兩種散斑活性指標(biāo),建立豬肉新鮮度LDA線性判別模型。結(jié)果顯示,465 nm波長下兩種指標(biāo)的單主成分判別模型,其識(shí)別率都要高于660 nm,說明465 nm波長更能反映豬肉新鮮度變化。當(dāng)選擇兩種波長下的IM值和465 nm波長下第21幀及第201幀散斑圖像的Ckτ值四個(gè)特征參數(shù)作為主成分建模時(shí),模型的識(shí)別率最好。其訓(xùn)練集和預(yù)測(cè)集能分別達(dá)到95.31%和96.88%,且能完全識(shí)別腐敗肉樣本,因此利用激光散斑技術(shù)檢測(cè)冷鮮豬肉新鮮度的方法具有可行性。
[Abstract]:Cold fresh pork is the main meat consumption variety in people's daily life. People pay more and more attention to pork quality because of the frequent food safety incidents. The traditional methods of pork freshness detection are mainly sensory assessment and laboratory physical and chemical testing. The former is unstable, while the latter is time-consuming and difficult to meet the needs of consumers because it is not suitable for quick detection on the spot. The laser speckle technique is fast, non-destructive and simple. In this study, the laser speckle technique was applied to the detection of freshness of chilled pork. The specific research contents are as follows: 1. Based on the research results of laser speckle technology by domestic and foreign scholars, The whole test scheme of laser speckle detection for freshness of chilled pork was determined, the laser speckle test device was designed and built, and the batch acquisition and processing program of speckle image based on VS2013 MFC was compiled. The optimum experimental parameters were determined by pre-experiment. The laser wavelength was 465 nm and 660 nm, the laser power was 15.7 MW, the acquisition time was 60 Ms, and the incident angle was 30 擄. The selection of different speckle images will affect the IM value, and the traditional IM algorithm is greatly disturbed by the outliers. For this reason, three improvements are put forward: a sorting algorithm is designed to dynamically select the highest peak of speckle activity and two adjacent rows around it. Based on this, the IM value of the sample is calculated; the modified matrix calculation method of symbiosis matrix is improved; and the calculation method of non-zero element deviation from diagonal distance is improved. The results show that the sorting algorithm can locate the peak position of IM quickly. The improved method can effectively suppress the abnormal value interference, and can reflect the activity difference of the sample. When the change of water loss of pork was smaller than 0.14g / d, the effect on the speckle activity of the sample was smaller, but the change of water loss was more than 2 g / d, when the change of TVB-N content, the moment of inertia (IM) and the correlation number of C _ k 蟿 were analyzed, the results showed that, when the change of pork water loss was less than 0.14g / d, the change of sample speckle activity was less, but the change of water loss was more than 2 g / d. The loss of moisture resulted in a significant decrease in speckle activity, which indicated that moisture was the main reason for the change of speckle activity, and there was a positive correlation between the color of pork surface and the index of speckle activity. The correlation between a * value and speckle activity index was the most significant, which indicated that the speckle activity could reflect the freshness of pork. The TVB-N content of pork was negatively correlated with the speckle activity index during cold storage. Therefore, speckle activity alone can not accurately predict the content of TVB-N. The wavelength of 465nm and 660nm is selected, based on two speckle activity indexes of IM and Ck 蟿, The LDA linear discriminant model of pork freshness was established. The results showed that the single principal component discriminant model of two indexes was established at 465nm. The recognition rates are higher than 660 nm, indicating that 465nm wavelength can better reflect the variation of pork freshness. When selecting the IM value of two wavelengths and the four characteristic parameters of frame 21 and frame 201 speckle image at 465nm as principal component modeling, The model has the best recognition rate, its training set and prediction set can reach 95.31% and 96.88, respectively, and it can fully identify the rotten meat samples. Therefore, the method of detecting the freshness of cold fresh pork by laser speckle technique is feasible.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O657.3;TS251.51

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