Detection of l -Cysteine in wheat flour by Raman microspectroscopy combined chemometrics of HCA and PCA

Çebi, Nur; Doğan, Canan Ekinci; Develioglu, Ayşen; Yayla, Mediha Esra Altuntop; Sağdıç, Osman

doi:10.1016/j.foodchem.2017.01.132

Cited by 71 publications

(39 citation statements)

References 26 publications

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“…Also, L‐cysteine is additive that is forbidden in wheat flour in many countries. Because of compositional differences of adulterated flour samples, Cebi and others () selected 8 feature bands (1426, 1399, 1344, 1200, 823, 774, 693, and 640 cm −1 ) (Figure ) related to L‐cysteine. They concluded that the L‐cysteine could be determined in wheat flour with 100% success by using Raman HSI combined with chemometrics of PCA and hierarchical cluster analyses (HCA).…”

Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

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Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

138

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Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

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Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

“…(A) Raman spectra of L‐cysteine and wheat flour, (B) comparing Raman spectra of L‐cysteine, flour, and adulterated sample (Cebi and others ).…”

Section: Quality Evaluation Of Powdery Foodsmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

138

View full text Add to dashboard Cite

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“…Existing methods for feature extraction of fingerprint data mostly adopt principal component analysis (PCA), which is commonly used as auxiliary means before modeling and fails to get clear feature information . Therefore, this paper aims to study the method of feature extraction, so as to provide a key and reliable data source for the rapid and accurate identification of rice varieties.…”

Section: Introductionmentioning

confidence: 99%

Improving Raman spectroscopic identification of rice varieties by feature extraction

Sha

Zhang

et al. 2020

J Raman Spectroscopy

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Raman spectroscopy combined with pattern recognition can identify rice varieties excellently. In this study, a method was established to select the key feature for identification model. Seventy‐two Raman spectra of three varieties of rice were analyzed. Seventy‐one independent variables and characteristic bands (420–560, 820–980, 1,000–1,200, and 1,300–1,500 cm−1) were obtained by principal component analysis (PCA). Window analysis further narrowed the range of characteristic bands (451–550, 951–1,000, and 1,351–1,450 cm−1). Hierarchical cluster analysis (HCA) obtained 30 wavenumbers with small correlation. The prediction accuracy was 91.71%, whereas the time was reduced by 10 times when these 30 wavenumbers were used to establish the identification model. The method combined PCA, window analysis, and HCA with support vector machine can be used as an effective feature extraction method to improve the efficiency for identification of rice varieties. Under the circumstances of large sample size or relatively complex data, the screening of Raman spectrum information is an important means to simplify the model and improve the prediction efficiency.

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“…Cluster analysis (CA) is the unsupervised classification of patterns (feature vectors) into groups, so that individuals within the same group are more similar to each other than those belonging to different groups (Cebi, Dogan, Develioglu, Yayla, & Sagdic, ). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were carried out on the entire data obtained from the HS‐SPME‐GC‐MS in an attempt to identify the similarity of the samples.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive analysis of volatile compounds in cold‐pressed safflower seed oil from Xinjiang, China

Lin

Chen

Han

et al. 2019

Food Science & Nutrition

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Three varieties of safflower seed oil (SSO) from Xinjiang Autonomous Region, China, were analyzed by headspace solid‐phase micro‐extraction gas chromatography coupled with mass spectrometry (HS‐SPME‐GC‐MS) to reveal volatile components. Overall, 67 volatile components were determined and four compounds including isoamyl alcohol, caproic acid, n‐pentanal, and heptanal were newly identified in SSO as aroma‐active components. Meanwhile, 16 compounds were selected by relative odor activity value (ROAV) to evaluate contributions of single compounds to the overall odor (ROAV > 1), in which nonanal, (Z)‐6‐nonenal, and (E)‐2,4‐decadienal were the top three contributed substances (ROAV > 70). The sensory panel was described as eight definition terms (grassy, fruity, almond, mushroom, fatty, sweet, paddy, and overall fragrance). Principal component analysis (PCA) revealed a significant separation of three cultivars with the first principal component (PC‐1) and the second principal component (PC‐2) expressing 73.9% and 23.1%, respectively. Both PCA and ROAV allowed identifying the compounds positively correlated to sensory evaluation.

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Detection of l -Cysteine in wheat flour by Raman microspectroscopy combined chemometrics of HCA and PCA

Cited by 71 publications

References 26 publications

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Improving Raman spectroscopic identification of rice varieties by feature extraction

Comprehensive analysis of volatile compounds in cold‐pressed safflower seed oil from Xinjiang, China

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