KNN算法在動(dòng)物油鑒別區(qū)分中的應(yīng)用研究
發(fā)布時(shí)間：2019-05-15 00:28
【摘要】：氣相色譜-質(zhì)譜聯(lián)用法(GC-MS)因其分離效率高、分析速度快、靈敏度高、檢測(cè)線低等特點(diǎn),被廣泛應(yīng)用于油脂分析鑒別領(lǐng)域。但與礦物油的鑒別相比,動(dòng)物油類(lèi)之間的主成分種類(lèi)相近且含量集中,單純通過(guò)GC-MS進(jìn)行鑒別分析具有局限性,因此,區(qū)分常見(jiàn)動(dòng)物油一直是司法鑒定領(lǐng)域中的難題。本文嘗試運(yùn)用GC/MS分析人油和5種常見(jiàn)動(dòng)物油,通過(guò)對(duì)峰面積歸一化法得出每個(gè)樣品脂肪酸相對(duì)百分含量,結(jié)合KNN算法(K Nearest Neighbors,KNN)對(duì)人油與常見(jiàn)動(dòng)物油進(jìn)行建模區(qū)分。本實(shí)驗(yàn)以每個(gè)動(dòng)物油脂樣本中的6個(gè)主要脂肪酸相對(duì)含量(C14:0、C16:0、C16:1、C18:0、C18:1、C18:2)作為變量值,運(yùn)用訓(xùn)練樣本即為測(cè)試樣本的方法進(jìn)行交互驗(yàn)證,發(fā)現(xiàn)當(dāng)k值等于3或4時(shí),測(cè)試樣本出錯(cuò)率最低,區(qū)分效果良好,人油測(cè)試樣本分類(lèi)準(zhǔn)確率達(dá)到100%,并考察了6種脂肪酸相對(duì)含量作為變量的區(qū)分貢獻(xiàn)值,結(jié)果C14:0區(qū)分貢獻(xiàn)值最大。此方法相對(duì)于傳統(tǒng)分析手段而言簡(jiǎn)單易行,提高了鑒別分析的效率和精度,盡管實(shí)驗(yàn)樣本種類(lèi)有限,但實(shí)驗(yàn)方法具有普遍意義。本文為動(dòng)物油區(qū)分的進(jìn)一步深入研究提供了一種新的思路和參考。
[Abstract]:Gas chromatography-mass spectrometry (GC-MS) has been widely used in the field of oil analysis and identification because of its high separation efficiency, fast analysis speed, high sensitivity and low detection line. However, compared with the identification of mineral oil, the principal components of animal oil are similar and concentrated, and the identification analysis by GC-MS alone has limitations. Therefore, distinguishing common animal oil has always been a difficult problem in the field of judicial identification. In this paper, GC/MS is used to analyze human oil and five kinds of common animal oil, and the relative fatty acid content of each sample is obtained by normalizing the peak area. Combined with KNN algorithm (K Nearest Neighbors,KNN), the human oil and common animal oil are modeled and distinguished. In this experiment, the relative contents of six major fatty acids in each animal oil sample (C14, C16, C18, C1, c18, c18) were taken as variable values, and the relative contents of 6 major fatty acids in each animal oil sample (C14, C16, C18, C1, c18) were used as variable values. When the k value is 3 or 4, the error rate of the test sample is the lowest, the distinguishing effect is good, and the classification accuracy of the human oil test sample is 100%. The differential contribution values of the relative contents of six fatty acids as variables were investigated, and the results showed that the differential contribution value of C14 鈮，
本文編號(hào)：2477168
[Abstract]:Gas chromatography-mass spectrometry (GC-MS) has been widely used in the field of oil analysis and identification because of its high separation efficiency, fast analysis speed, high sensitivity and low detection line. However, compared with the identification of mineral oil, the principal components of animal oil are similar and concentrated, and the identification analysis by GC-MS alone has limitations. Therefore, distinguishing common animal oil has always been a difficult problem in the field of judicial identification. In this paper, GC/MS is used to analyze human oil and five kinds of common animal oil, and the relative fatty acid content of each sample is obtained by normalizing the peak area. Combined with KNN algorithm (K Nearest Neighbors,KNN), the human oil and common animal oil are modeled and distinguished. In this experiment, the relative contents of six major fatty acids in each animal oil sample (C14, C16, C18, C1, c18, c18) were taken as variable values, and the relative contents of 6 major fatty acids in each animal oil sample (C14, C16, C18, C1, c18) were used as variable values. When the k value is 3 or 4, the error rate of the test sample is the lowest, the distinguishing effect is good, and the classification accuracy of the human oil test sample is 100%. The differential contribution values of the relative contents of six fatty acids as variables were investigated, and the results showed that the differential contribution value of C14 鈮，
本文編號(hào)：2477168
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