西式快餐用煎炸油质量近红外快速检测模型的建立

胡明明; 张权; 宁舒娴; 吴思纷; 张国文; 胡兴

doi:10.13386/j.issn1002-0306.2021120181

西式快餐用煎炸油质量近红外快速检测模型的建立

doi: 10.13386/j.issn1002-0306.2021120181

胡明明^{1, 2, 3,},
张权²,
宁舒娴²,
吴思纷²,
张国文^3, ,,
胡兴³

1.
江西师范大学国家淡水鱼加工技术研发专业中心，江西南昌 330022
2.
江西师范大学生命科学院，江西南昌 330022
3.
南昌大学食品科学与技术国家重点实验室，江西南昌 330047

基金项目: 国家自然科学青年基金项目（31801470）；江西省教育厅科学技术研究项目（GJJ210322）；江西省青年科学基金项目（20192BAB214011）。

详细信息

作者简介:
胡明明（1986−），男，博士，助理研究员，研究方向：油脂质量分析及产品开发应用，E-mail：2006abc-hmm@163.com

通讯作者:
张国文（1966−），男，博士，教授，研究方向：食品化学与分析、食品营养与安全，E-mail：gwzhang@ncu.edu.cn

中图分类号: TS225.1
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- 被引次数: 0
出版历程
- 收稿日期: 2021-12-20
- 网络出版日期: 2022-04-18
- 刊出日期: 2022-06-01

Study on Quick Test Model for the Quality of Frying Oil from Western-style Fast Food Restaurants by Near Infrared Spectroscopy

HU Mingming^{1, 2, 3
,},
ZHANG Quan²,
NING Shuxian²,
WU Sifen²,
ZHANG Guowen^{3
, ,},
HU Xing³

1.
National R & D Center for Freshwater Fish Processing, Jiangxi Normal University, Nanchang 330022, China
2.
College of Life Science, Jiangxi Normal University, Nanchang 330022, China
3.
State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China

摘要

摘要: 为对西式快餐用煎炸油质量进行快速监测，选用近红外光谱法（NIRS）联合偏最小二乘法（PLS），分别建立酸价和极性组分两个煎炸油质量指标的定量模型。结果表明，酸价和极性组分的定标模型校正决定系数（R²）均为0.9974，校正标准差均方根（RMSEC）分别为0.111和0.359，预测标准差均方根（RMSEP）分别为0.171和0.562。盲样验证、精密度及准确度分析结果显示，酸价和极性组分的NIR预测值同真实值的相关方程的相关系数分别为0.9944、0.9761，应用所建模型预测同一煎炸油样品的酸价和极性组分的相对标准偏差（RSD）分别为0.934%和1.278%，表明所建模型对煎炸油样品的酸价和极性组分预测能力较好，且有较好的重现性。因此，基于近红外光谱定量模型，可以对西式快餐用煎炸油质量进行快速、准确地监测。
- 煎炸油 /
- 近红外光谱 /
- 酸价 /
- 极性组分
Abstract: In order to quickly detect the quality of frying oil from western-style fast food restaurants, calibration models of acid value and total polar compounds were established using near infrared spectroscopy (NIRS) combined with partial least squares (PLS) to evaluate the frying oil quality. The findings presented that the correction coefficient of calibration model of acid value and total polar compounds were both 0.9974. The root mean square error of cross-validation (RMSEC) were 0.111 and 0.359, respectively. The root mean square error of prediction (RMSEP) were 0.171 and 0.562, respectively. The results of blind samples verification showed that the correlation coefficients between prediction value and true value for acid value and total polar compounds of frying oil were 0.9944 and 0.9761, respectively; the precision test showed that the relative standard deviations (RSD) of acid value and total polar components using the calibration models for the same frying oil sample were 0.934% and 1.278%, respectively, which indicated that the models of quantitative analysis of acid value and total polar compounds of frying oils were good with excellent prediction abilities and reproducibility. Therefore, the rapid detection model based on near infrared spectroscopy could rapidly and accurately detect the quality of frying oil from western-style fast food restaurants.
- frying oil /
- near infrared spectroscopy /
- acid value /
- total polar compounds

HTML全文

图 1 101 个煎炸油样品近红外光谱图

Figure 1. The NIR spectra of 101 frying oil samples

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图 2 近红外一阶导数光谱图

Figure 2. NIR spectra by first derivative

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图 3 煎炸油样品的马氏距离分布图

Figure 3. Mahalanobis distance of frying oil samples

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图 4 酸价定量分析模型 RMSECV 随主因子数的变化

Figure 4. Changes in RMSECV with principal factors in quantitative analysis model of acid value

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图 5 极性组分定量分析模型 RMSECV 随主因子数的变化

Figure 5. Changes in RMSECV with principal factors in quantitative analysis model of total polar compounds

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图 6 酸价的近红外光谱 PLS 建模结果图（a）和偏差图（b）

Figure 6. Correlation (a) and deviation (b) between NIR predicted values and reference values of acid value

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图 7 极性组分的近红外光谱 PLS 建模结果图（a）和偏差图（b）

Figure 7. Correlation (a) and deviation (b) between NIR predicted values and reference values of total polar compounds

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图 8 煎炸油酸价（a）和极性组分（b）预测值与真实值分布

Figure 8. Prediction value and true value of acid value (a) and total polar compounds (b) of frying oil

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表 1 煎炸油定量分析模型校正集和验证集酸价和极性组分的分布

Table 1. Acid value and total polar compounds content of calibration set and validation set of quantitative analysis model for frying oil

样品类别	项目	校正集（88个）	验证集（13个）
酸价（mg KOH/g）	范围	0.11~8.96	0.23~5.22
酸价（mg KOH/g）	均值	1.56	1.81
极性组分（%）	范围	3.83~28.00	7.33~27.67
极性组分（%）	均值	13.48	13.66

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表 2 不同光谱预处理对煎炸油酸价定量分析模型的影响

Table 2. Effect of various pretreatment methods on the performance of quantitative analysis model of acid value for frying oil

预处理方法	R²	RMSEC	RMSEP
无处理	0.9966	0.125	0.142
一阶导数	0.9956	0.146	0.343
一阶导数+S-G平滑	0.9940	0.167	0.254
一阶导数+Norris平滑	0.9961	0.134	0.131
二阶导数	0.2197	1.490	1.320
二阶导数+S-G平滑	0.9834	0.277	0.577
二阶导数+Norris平滑	0.9974	0.111	0.171

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表 3 不同光谱预处理对煎炸油极性组分定量分析模型的影响

Table 3. Effect of various pretreatment methods on the performance of quantitative analysis model of total polar compounds for frying oil

预处理方法	R²	RMSEC	RMSEP
无处理	0.9956	0.468	0.706
一阶导数	0.9959	0.453	1.130
一阶导数+S-G平滑	0.9956	0.464	1.010
一阶导数+Norris平滑	0.9974	0.359	0.562
二阶导数	0.9445	1.630	3.090
二阶导数+S-G平滑	0.9933	0.576	1.280
二阶导数+Norris平滑	0.9967	0.402	0.652

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参考文献(32)

[1]	HU M M, ZHU M, XIN L, et al. Change of benzo (a) pyrene during frying and its groove binding to calf thymus DNA[J]. Food Chemistry,2021,350:129276. doi: 10.1016/j.foodchem.2021.129276
[2]	LI X, WU X J, LIU R J, et al. Effect of frying conditions on fatty acid profile and total polar materials via viscosity[J]. Food Chemistry,2015,166:349−355.
[3]	LI X D, LI J W, WANG Y, et al. Effects of frying oils’ fatty acids profile on the formation of polar lipids components and their retention in French fries over deep-frying process[J]. Food Chemistry,2017,237:98−105. doi: 10.1016/j.foodchem.2017.05.100
[4]	KOH E, SURH J. Food types and frying frequency affect the lipid oxidation of deep frying oil for the preparation of school meals in Korea[J]. Food Chemistry,2015,174:467−472. doi: 10.1016/j.foodchem.2014.11.087
[5]	ZHU Y, LI X, HUANG J H, et al. Correlations between polycyclic aromatic hydrocarbons and polar components in edible oils during deep frying of peanuts[J]. Food Control,2018,87:109−116. doi: 10.1016/j.foodcont.2017.12.011
[6]	ALADEDUNYE F, PRZYBYLSKI R. Performance of palm olein and modified rapeseed, sunflower, and soybean oils in intermittent deep-frying[J]. European Journal of Lipid Science and Technology,2014,116:144−152. doi: 10.1002/ejlt.201300284
[7]	HAO X W, LI J, YAO Z L. Changes in PAHs levels in edible oils during deep-frying process[J]. Food Control,2016,66:233−240. doi: 10.1016/j.foodcont.2016.02.012
[8]	WAGHMAREA A, PATILA S, LEBLANC J G, et al. Comparative assessment of algal oil with other vegetable oils for deep frying[J]. Algal Research,2018,31:99−106. doi: 10.1016/j.algal.2018.01.019
[9]	SANTOS C S P, MOLINA-GARCIA L, CUNHA S C, et al. Fried potatoes: Impact of prolonged frying in monounsaturated oils[J]. Food Chemistry,2018,243:192−201. doi: 10.1016/j.foodchem.2017.09.117
[10]	RAHIMI J, ADEWALE P, NGADI M, et al. Changes in the textural and thermal properties of batter coated fried potato strips during post frying holding[J]. Food and Bioproducts Processing,2017,102:136−143. doi: 10.1016/j.fbp.2016.12.013
[11]	中华人民共和国卫生部. GB 5009.202-2016. 食品安全国家标准食用油中极性组分(PC)的测定[S]. 北京: 中国标准出版社, 2016 Ministry of Health of the People's Republic of China. GB 5009.202-2016. National standards of China-Determination of polar component (PC) in edible oil [S]. Beijing: China Standards Press, 2016.
[12]	万毅, 张玉, 杨华, 等. 基于近红外光谱的橄榄油理化指标快速检测模型研究[J]. 食品工业科技,2017,38(21):257−262. [WANG Y, ZHANG Y, YANG H, et al. Study on rapid detection model of physical and chemical indexes of olive oil based on near infrared spectroscopy[J]. Science and Technology of Food Industry,2017,38(21):257−262.
[13]	汪鑫, 田花丽, 马卓, 等. 近红外光谱技术在油料作物快速检测中的应用研究进展[J]. 食品研究与开发,2021,42(22):220−224. [WANG X, TIAN H L, MA Z, et al. Research process in application of near infrared spectroscopy in rapid detection of oil crops[J]. Science and Technology of Food Industry,2021,42(22):220−224.
[14]	刘爽, 柴春祥. 近红外光谱技术在水产品检测中的应用进展[J]. 食品安全质量检测学报,2021,12(21):8590−8596. [LIU S, CHAI C X. Application progress of near infrared spectroscopy in aquatic products detection[J]. Food Safety and Quality Detection Technology,2021,12(21):8590−8596.
[15]	褚小立, 史云颖, 陈瀑, 等. 近五年我国近红外光谱分析技术研究与应用进展[J]. 分析测试学报,2019,38(5):603−611. [CHU X L, SHI Y Y, CHEN P, et al. Research and application progresses of near infrared spectroscopy analytical technique in China in past five years[J]. Journal of Instrumental Analysis,2019,38(5):603−611. doi: 10.3969/j.issn.1004-4957.2019.05.016
[16]	TEIXEIRA DOS SANTOS C A, LOPO M, PÁSCOA R N M J, et al. A review on the applications of portable near-infrared spectrometers in the agro-food industry[J]. Applied Spectroscopy,2013,67(11):1215−1233. doi: 10.1366/13-07228
[17]	HELL J, PRÜCKLER M, DANNER L, et al. A comparison between near-infrared (NIR) and mid-infrared (ATR-FTIR) spectroscopy for the multivariate determination of compositional properties in wheat bran samples[J]. Food Control,2016,60:365−369. doi: 10.1016/j.foodcont.2015.08.003
[18]	张勇, 王督, 李雪, 等. 基于近红外光谱技术的农产品产地溯源研究进展[J]. 食品安全质量检测学报,2018,9(23):6161−6166. [ZHANG Y, WANG D, LI X, et al. Research progress of near infrared spectroscopy based geographical origin traceability of agricultural products[J]. Food Safety and Quality Detection Technology,2018,9(23):6161−6166. doi: 10.3969/j.issn.2095-0381.2018.23.015
[19]	SINELLI N, CASALE M, EGIDIO V D, et al. Varietal discrimination of extra virgin olive oils by near and mid infrared spectroscopy[J]. Food Research International,2010,43(8):2126−2131. doi: 10.1016/j.foodres.2010.07.019
[20]	BASRI K N, HUSSAIN M N, BAKAR J, et al. Classification and quantification of palm oil adulteration via portable NIR spectroscopy[J]. Spectrochimica Acta Part A: Molecular & Biomolecular Spectroscopy,2017,173:335−342.
[21]	张青青, 沈晓芳, 马晶晶, 等. 近红外光谱法快速分析马铃薯煎炸油的品质[J]. 中国油脂,2019,44(1):132−136. [ZHANG Q Q, SEHN X F, MA J J, et al. Rapid analysis of oil quality in potato frying system by near-infrared spectroscopy[J]. China Oils and Fats,2019,44(1):132−136. doi: 10.3969/j.issn.1003-7969.2019.01.029
[22]	HU M M, PAN K L, NIU Y T, et al. Comparative assessment of thermal resistance of palm stearin and high oleic blended oil when subjected to frying practice in fast food restaurants[J]. Journal of Oil Palm Research,2020,32(1):90−102.
[23]	WANG L, LEE F S C, WANG X R, et al. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR[J]. Food Chemistry,2006,95(3):529−536. doi: 10.1016/j.foodchem.2005.04.015
[24]	CHRISTY A A, KASEMSUMRAN S, DU Y P, et al. The detection and quantifcation of aduteration in olive oil by near-infrared spectroscopy and chemometrics[J]. Analytical Sciences,2004,20:935−940. doi: 10.2116/analsci.20.935
[25]	胡小莉, 白雁, 雷敬卫, 等. NIRS结合TQ软件对不同产地野菊花定性定量分析[J]. 中国实验方剂学杂志,2016,22(15):37−41. [HU X L, BAI Y, LEI J W, et al. Qualitative and quantitative analysis of Chrysanthemi Indici Flos from different origins by using NIRS and TQ software[J]. Chinese Journal of Experimental Traditional Medical Formulae,2016,22(15):37−41.
[26]	冯国霞. 西式快餐用高油酸型煎炸油的研究与应用[D]. 无锡: 江南大学, 2015 FENG G X. Research and application of high-oleic frying oil for western-style fast food industry[D]. Wuxi: Jiangnan University, 2015.
[27]	闵顺耕, 李宁, 张明祥. 近红外光谱分析中异常值的判别与定量模型优化[J]. 光谱学与光谱分析,2004,24(10):1205−1209. [MIN S G, LING N, ZHANG M X, et al. Outlier diagnosis and calibration model optimization for near infrared spectroscopy analysis[J]. Spectroscopy and Spectral Analysis,2004,24(10):1205−1209. doi: 10.3321/j.issn:1000-0593.2004.10.014
[28]	赵怡锟, 于燕波, 申兵辉, 等. 近红外光谱分析在玉米单籽粒品种真实性鉴定中的影响因素[J]. 光谱学与光谱分析,2020,40(7):2229−2234. [ZHAO Y K, YU Y B, SHEN B H, et al. Influence factors in near-infrared spectrum analysis for the authenticity identification of maize single-kernel varieties[J]. Spectroscopy and Spectral Analysis,2020,40(7):2229−2234.
[29]	SHETTY N, DIFFORD G, LASSEN J, et al. Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk[J]. Journal of Dairy Science,2017,100(11):9052−9060. doi: 10.3168/jds.2017-13014
[30]	RINNAN A, VAN DEN BERG F, ENGELSEN S B. Review of the most common pre-processing techniques for near-infrared spectra[J]. TrAC Trends in Analytical Chemistry,2009,28(10):1201−1222. doi: 10.1016/j.trac.2009.07.007
[31]	ISAKSSON T, NAES T. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy[J]. Applied Spectroscopy,1988,42(7):1273−1284. doi: 10.1366/0003702884429869
[32]	冯利辉. 食用植物油掺伪检测与定量分析的近红外光谱研究[D]. 南昌: 南昌大学, 2010 FENG L H. Detection of adulteration and quantilation analysis of edible vegetable oil by near infrared spectrcosopy[D]. Nanchang: Nanchang University, 2010.