采用化学法测定紫胶理化指标的化学值,应用傅里叶变换近红外光谱技术,采集紫胶的近红外光谱并使用光谱预处理方法消除噪声,组合区间偏最小二乘法选择特征波段,采用内部交互验证法筛选主成分数,最后通过偏最小二乘法建立回归模型,最终得到紫胶中灰分、水分、冷乙醇可溶物、热寿命、酸值和颜色指数的近红外光谱定量分析模型。灰分、水分、冷乙醇可溶物、热寿命、酸值和颜色指数校正集校正决定系数分别为0.968、0.982、0.945、0.821、0.873和0.946,交叉验证标准误差分别为0.054、0.081、1.050、0.359、1.230和1.880;验证集的决定系数分别为0.958、0.981、0.904、0.810、0.872和0.930,预测标准误差分别为0.039、0.039、0.039、0.234、0.700和0.618;相对分析误差值分别为5.58、7.65、3.30、2.51、2.82和4.31。结果表明,近红外光谱法对热寿命和酸值进行定量分析是可行的,但其精度有待进一步提高。对于紫胶中灰分、水分、冷乙醇可溶物和颜色指数,内部交叉验证和外部验证均证明,建立的近红外定量分析模型的准确度和预测性能良好,为紫胶理化指标的快速分析方法的研究提供了新的参考。
Chemical methods were used to determine the chemical values of physicochemical properties of shellac. The application of Fourier transform near-infrared spectrum technology was performed to collect near infrared spectra of shellac and the spectral noise eliminated by pretreatment method. And synergy interval partial least (SIPLS) was employed to choose the characteristics of the band and the internal interaction validation method screening principal components number. Finally, the index of physicochemical properties including ash, moisture, cold alcohol soluble, thermal life, acid value and color index of near infrared spectrum quantitative analysis model was established by partial least squares (PLS) regression algorithm. The results showed that the correction determination coefficients (R2c) of ash, moisture, cold alcohol soluble, thermal life, acid value and color index were 0.968, 0.982, 0.945, 0.821, 0.873 and 0.946, respectively. And the root mean square error of cross validation (RMSECV) was 0.054, 0.081, 1.050, 0.359, 1.230 and 1.880, respectively. Moreover, the determination coefficient (R2p) of validation sets were 0.958, 0.981, 0.904, 0.810, 0.872 and 0.930 for these indexes, and the prediction standard errors (RMSEP) were 0.039, 0.039, 0.039, 0.234, 0.700 and 0.618, respectively. Besides, the relative percent deviation (RPD) values were 5.58, 7.65, 3.30, 2.51, 2.82 and 4.31. Therefore, the near infrared spectroscopy was feasible for the quantitative analysis of thermal life and acid value. But its precision needs to be further improved. For ash, moisture content, cold alcohol soluble and color index of shellac, internal cross validation and external validation sets all proved that the accuracy and prediction performance of the established near-infrared quantitative analysis model were good, which provided a new reference for the research of rapid analysis method of physical and chemical indexes of shellac.
[1] 陈晓鸣, 陈又清, 张弘. 紫胶虫培育与紫胶加工[M]. 北京:中国林业出版社, 2008:1-39.
[2] ANSARI M F, SARKHEL G. Improving coating properties of shellac-epoxidised-novolac blends with melamine formaldehyde resin[J]. Pigment & Resin Technology, 2017, 46(2): 92-99.
[3] KUMAR V, GUPTA S, MISHRA N K, et al. Laser-induced fabrication of gold nanoparticles on shellac-driven peptide nanostructures[J]. Materials Research Express, 2017, 4(3): 035 036.
[4] KHAIRUDDI N, PRAMONO E, UTOMO S B, et al. FTIR studies on the effect of concentration of polyethylene glycol on polimerization of shellac[J]. Journal of Physics Conference Series, 2016, 776 (1): 012 053.
[5] 李凯, 张弘, 郑华, 等. 紫胶树脂改性研究进展[J]. 天然产物研究与开发, 2012, 24(2):274-279.
[6] THAWATCHAI P, SETTHAPONG S, NAPAPHOL P, et al. Solvent exchange and drug release characteristics of doxycycline hyclate-loaded bleached shellac in situ-forming gel and microparticle[J]. International Journal of Biological Macromolecules, 2019, 135: 1 261-1 272.
[7] 潘正东, 李凯, 徐涓, 等. 响应面试验优化紫胶树脂钠盐为壁材制备VE微胶囊工艺[J].食品科学, 2016, 37(12):19-26.
[8] LI K, PAN Z D, GUAN C, et al. A tough self-assembled natural oligomer hydrogelbased on nano-size vesicle cohesion[J]. RSC Advances, 2016, 6(40): 33 547-33 553.
[9] 张雯雯, 毕兴丹, 唐勇, 等. 攀枝花地区紫胶组分分析[J]. 东北林业大学学报, 2010, 38(11): 119-121.
[10] SORADECH S, LIMMATVAPIRAT S, LUANGTANA-ANAN M. Stability enhancement of shellac by formation of composite film Effect of gelatin and plasticizers[J]. Journal of Food Engineering, 2013, 116(2):572-580.
[11] 马李一, 甘瑾, 殷宁, 等. 天然涂膜保鲜剂对青脆李的贮藏保鲜作用[J]. 食品与发酵工业, 2004, 30(7):135-138.
[12] PHAECHAMUD T, MAHADLEK J, CHUENBARN T. In situ forminggel comprising bleached shellac loaded with antimicrobialdrugs for periodontitis treatment[J]. Materials and Design, 2016, 89: 294-303.
[13] WETHTHIMUNI M L, CAPSONI D, MALAGODI M, et al. Shellac/nanoparticles dispersions as protective materials for wood[J]. Applied Physics A, 2016, 122(12): 1 058.
[14] 沈广辉, 刘贤, 张月敬, 等. 基于在线近红外光谱快速检测玉米籽粒主要品质参数的研究[J]. 中国畜牧杂志, 2017, 53(1): 105-109.
[15] CHEN H, LIN Z, TAN C. Automatic cancer discrimination based on near-infrared spectrum and class-modeling technique[J]. Vibrational Spectroscopy, 2020, 106:102 991.
[16] 朱华, 吴珽, 房桂干, 等. 近红外技术的广西速生桉抽出物含量测定与模型优化[J].光谱学与光谱分析, 2020, 40(3):793-798.
[17] GU C Y, TANG Q Q, XIANG B R. Determination of fenitrothion in water by near infrared spectroscopy and chemometric analysis[J]. Analytical Letters, 2015, 48 (9):1 481-1 493.
[18] 买书魁,吴镇君,陈红光, 等. 基于近红外光谱技术的白酒原酒中关键成分的定量分析[J].食品与发酵工业, 2018, 44(11):280-285.
[19] 潘威, 马文广, 郑昀晔, 等. 用近红外光谱无损测定烟草种子淀粉含量[J].烟草科技, 2017, 50(2):15-21.
[20] 田翔, 刘思辰, 王海岗, 等. 近红外漫反射光谱法快速检测谷子蛋白质和淀粉含量[J]. 食品科学, 2017, 38(16):140-144.
[21] 于怀智, 陈东杰, 姜沛宏,等. 基于近红外光谱对蒙阴黄桃硬度和可溶性固形物的在线检测[J/OL].食品与发酵工业, DOI: 10.13995/j.cnki.11-1802/ts.022156.
[22] 盛晓慧, 李宗朋, 李子文, 等. 近红外光谱技术定量检测果味啤中的果汁含量[J].食品与发酵工业, 2020, 46(4):247-252.
[23] LIU Y, XIA Z Z, YAO L Y, et al. Discriminating geographic origin of sesame oils and determining lignans by near-infrared spectroscopy combined with chemometric methods[J]. Journal of Food Composition and Analysis, 2019, 84:103 327.
[24] 中华人民共和国国家质量监督检验检疫总局.中国国家标准化委员 GB/T 8143—2008紫胶产品检验方法[S].北京: 中国标准出版社, 2009.
[25] CAFFERKY J, SWEENEY T, ALLEN P, et al. Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M.longissimus thpracis et lumborum[J]. Meat Science, 2020, 159:107 915.
[26] 邹小波, 封韬, 郑开逸, 等.利用近红外及中红外融合技术对小麦产地和烘干程度的同时鉴别[J].光谱学与光谱分析, 2019, 39(5):1 445-1 450.
[27] 雷玉, 郭雪媚, 朱世超, 等. 近红外光谱检测技术在聚合物领域的应用研究进展[J].光谱学与光谱分析,2019, 39(7):2 114-2 118.
[28] TORMENA C D, MARCHEAFAVE G G, PAULI E D, et al. Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis[J]. Environmental Science and Pollution Research, 2019, 26(29): 30 356-30 364.
[29] 洛曲, 于修烛, 张建新, 等. 基于近红外光谱的藏区酥油脂肪和蛋白质含量快速检测分析[J].中国油脂, 2018, 43(3): 136-140.
[30] SCHLEGEL L B, SCHUBERT-ZSILAVECZ M, ABDEL-TAWAB M. Quantification of active ingredients in semi-solid pharmaceutical formulations by near infrared spectroscopy[J]. Pharm Biomed Anal. 2017, 142 (20): 178-189.
[31] 刘红梅, 肖正午, 申涛, 等. 稻米直链淀粉含量近红外检测模型的建立[J]. 湖南农业大学学报(自然科学版), 2019, 45(2): 189-193.
[32] CHEN L J, YANG Z L, HAN L J. A review on the use of near-infrared spectroscopy for analyzing feed protein materials[J]. Applied Spectroscopy Reviews, 2013, 48(7): 509-522.