Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis
The objective of this study was to develop a rapid technique to authenticate potato chip frying oils using vibrational spectroscopy signatures in combination with pattern recognition analysis. Potato chip samples (<i>n</i> = 118) were collected from local grocery stores, and the oil was...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-12-01
|
Series: | Foods |
Subjects: | |
Online Access: | https://www.mdpi.com/2304-8158/10/1/42 |
id |
doaj-b4d50ad141a84c84904e6e1c89d1b65e |
---|---|
record_format |
Article |
spelling |
doaj-b4d50ad141a84c84904e6e1c89d1b65e2020-12-26T00:02:13ZengMDPI AGFoods2304-81582021-12-0110424210.3390/foods10010042Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition AnalysisSiyu Yao0Didem Peren Aykas1Luis Rodriguez-Saona2Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USADepartment of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USADepartment of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, USAThe objective of this study was to develop a rapid technique to authenticate potato chip frying oils using vibrational spectroscopy signatures in combination with pattern recognition analysis. Potato chip samples (<i>n</i> = 118) were collected from local grocery stores, and the oil was extracted by a hydraulic press and characterized by fatty acid profile determined by gas chromatography equipped with a flame ionization detector (GC-FID). Spectral data was collected by a handheld Raman system (1064 nm) and a miniature near-infrared (NIR) sensor, further being analyzed by SIMCA (Soft Independent Model of Class Analogies) and PLSR (Partial Least Square Regression) to develop classification algorithms and predict the fatty acid profile. Supervised classification by SIMCA predicted the samples with a 100% sensitivity based on the validation data. The PLSR showed a strong correlation (Rval > 0.97) and a low standard error of prediction (SEP = 1.08–3.55%) for palmitic acid, oleic acid, and linoleic acid. 11% of potato chips (<i>n</i> = 13) indicated a single oil in the label with a mislabeling problem. Our data supported that the new generation of portable vibrational spectroscopy devices provided an effective tool for rapid in-situ identification of oil type of potato chips in the market and for surveillance of accurate labeling of the products.https://www.mdpi.com/2304-8158/10/1/42rapid authenticationhandheld RamanNIRfatty acid profileoil qualification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Siyu Yao Didem Peren Aykas Luis Rodriguez-Saona |
spellingShingle |
Siyu Yao Didem Peren Aykas Luis Rodriguez-Saona Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis Foods rapid authentication handheld Raman NIR fatty acid profile oil qualification |
author_facet |
Siyu Yao Didem Peren Aykas Luis Rodriguez-Saona |
author_sort |
Siyu Yao |
title |
Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis |
title_short |
Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis |
title_full |
Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis |
title_fullStr |
Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis |
title_full_unstemmed |
Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis |
title_sort |
rapid authentication of potato chip oil by vibrational spectroscopy combined with pattern recognition analysis |
publisher |
MDPI AG |
series |
Foods |
issn |
2304-8158 |
publishDate |
2021-12-01 |
description |
The objective of this study was to develop a rapid technique to authenticate potato chip frying oils using vibrational spectroscopy signatures in combination with pattern recognition analysis. Potato chip samples (<i>n</i> = 118) were collected from local grocery stores, and the oil was extracted by a hydraulic press and characterized by fatty acid profile determined by gas chromatography equipped with a flame ionization detector (GC-FID). Spectral data was collected by a handheld Raman system (1064 nm) and a miniature near-infrared (NIR) sensor, further being analyzed by SIMCA (Soft Independent Model of Class Analogies) and PLSR (Partial Least Square Regression) to develop classification algorithms and predict the fatty acid profile. Supervised classification by SIMCA predicted the samples with a 100% sensitivity based on the validation data. The PLSR showed a strong correlation (Rval > 0.97) and a low standard error of prediction (SEP = 1.08–3.55%) for palmitic acid, oleic acid, and linoleic acid. 11% of potato chips (<i>n</i> = 13) indicated a single oil in the label with a mislabeling problem. Our data supported that the new generation of portable vibrational spectroscopy devices provided an effective tool for rapid in-situ identification of oil type of potato chips in the market and for surveillance of accurate labeling of the products. |
topic |
rapid authentication handheld Raman NIR fatty acid profile oil qualification |
url |
https://www.mdpi.com/2304-8158/10/1/42 |
work_keys_str_mv |
AT siyuyao rapidauthenticationofpotatochipoilbyvibrationalspectroscopycombinedwithpatternrecognitionanalysis AT didemperenaykas rapidauthenticationofpotatochipoilbyvibrationalspectroscopycombinedwithpatternrecognitionanalysis AT luisrodriguezsaona rapidauthenticationofpotatochipoilbyvibrationalspectroscopycombinedwithpatternrecognitionanalysis |
_version_ |
1724370776084185088 |