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

HU Mingming; ZHANG Quan; NING Shuxian; WU Sifen; ZHANG Guowen; HU Xing

doi:10.13386/j.issn1002-0306.2021120181

Volume 43 Issue 11

May 2022

Turn off MathJax

Article Contents

Abstract

References

Science and Technology of Food Industry > 2022 > 43(11): 11-17. > DOI: 10.13386/j.issn1002-0306.2021120181

HU Mingming, ZHANG Quan, NING Shuxian, et al. Study on Quick Test Model for the Quality of Frying Oil from Western-style Fast Food Restaurants by Near Infrared Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(11): 11−17. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021120181.

Citation:

PDF (2695 KB)

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

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

More Information

Received Date: December 19, 2021
Available Online: April 06, 2022

Graphical Abstract

Abstract

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

FullText(HTML)

References (32)

References

[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.

Supplements (0)

Cited By

Cited by

Periodical cited type(18)

1.	温雅君，王全红，杨红菊，孙志伟，刘希艳，高利文，肖志勇. 胶体金免疫层析法快速检测韭菜中腐霉利的质量分析与评价. 农药科学与管理. 2025(01): 29-33 .
2.	杨静，方芳，沈媛，孙娟，吴仑，陈翔，贾晨，李英. 水产品中地西泮快速检测产品技术评价. 食品安全质量检测学报. 2025(07): 102-108 .
3.	姚南南，刘芳，高会群，张学龙，杜斌，张郢，杨梅，蒲小容. 基于荧光微球的多菌灵残留快速检测试纸条的研制. 食品科技. 2024(01): 344-350 .
4.	陈喆，高文分，刘屹. 快速显色法筛查祛斑美白类化妆品中糖皮质激素. 化学分析计量. 2024(07): 7-11+22 .
5.	薛芳，张照红，殷慧龄. 胶体金免疫层析法在农残快速检测中的研究与探讨. 新疆农业科技. 2024(06): 39-41 .
6.	骆丽清，伍浚铧，胡茗淇，黎喜萍. 氟虫腈和水胺硫磷胶体金试剂盒性能考察与分析. 食品安全导刊. 2024(35): 72-75 .
7.	杨睿，蔡琳，卢灿鑫，李乐诗，张洁吟，刘晓晗，王韦达. 致病菌测试片质量评价方法研究. 食品安全质量检测学报. 2024(23): 41-51 .
8.	陈振东. 食品安全快速检测技术在食品安全监督中的运用浅析. 食品安全导刊. 2023(03): 121-123 .
9.	王元清，周巧，李莎，韩静，王惠，李建龙，何利，陈姝娟，刘爱平，李琴，胡凯弟，刘书亮. 市售原粮农药残留快速检测产品的质量评价与分析. 中国粮油学报. 2023(04): 122-128 .
10.	刘海虹，刘耀慧，雷毅. 基于真实食品的兽药残留快检结果准确性验证及应用探索. 食品安全导刊. 2023(33): 63-67 .
11.	罗俊霞，张刚，申战宾，杨华，叶茂，段鹿梅，李艳珍，赵建波，桑丽雅，马蕾，张威. 胶体金免疫层析技术应用于农药残留检测的研究进展. 农产品质量与安全. 2022(01): 41-49 .
12.	倪诗瑶，刘欠欠. 草莓农药残留快速检测方法对比分析. 上海农业科技. 2022(01): 30-32 .
13.	顾晔，张爽，王成军，李悦，杨雨柔. 基于免疫原理的7种磺胺类兽药残留快速检测试剂结果准确性评估. 食品安全质量检测学报. 2022(03): 992-1000 .
14.	陈振东. 食品安全检测技术在保障食品质量安全中的作用. 食品安全导刊. 2022(35): 166-168 .
15.	叶秋雄，毛新武，梁俊发，张彬彬，林嘉健，彭程，易云婷. 农贸市场食用农产品快速检测工作监督评价与效果分析. 食品安全质量检测学报. 2021(19): 7826-7830 .
16.	泮秋立，胡明燕，沈祥震，孙嵛林，李峥，王骏. 食用农产品批发市场自建快检室运行中存在的问题及建议. 食品安全导刊. 2021(29): 7-10 .
17.	占绣萍，刘彬，黄兰淇，陈秀，马琳，陈建波，赵莉. 应用胶体金法检测叶类蔬菜中吡虫啉、多菌灵、啶虫脒、噻虫嗪的残留量分析. 农药科学与管理. 2021(10): 24-31 .
18.	岳绪辉，杜斌，林栋，令狐克勇，杨梅，付秋平，李丙凤，杨曦. 草甘膦胶体金免疫层析试纸条的研制. 食品科技. 2021(12): 301-307 .