A feasibility quantification study of capsaicin content in chili powder for rapid evaluation using near-infrared reflectance spectroscopy

Jing, Bowen; Song, W. M.; Gao, Xin; He, Ke; Sun, Qinming; Tang, Xiuying

doi:10.1007/s11694-023-01965-x

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Thus, the capsaicin content increased as the pericarp percentage increased. Capsaicin (C 18 H 27 NO 3 ) is an inherent component of red peppers, and many previous studies reported that capsaicin content affects the NIR spectra of red pepper powder [ 23 , 24 ]. The characteristic wavelengths of chili pepper capsaicin are 1035 nm (the second overtone of N–H), 1150–1210 nm (the second overtone absorption of C–H stretching), 1300–1500 nm (the second combination regions of C–H stretching), and 1530 nm (the first overtone of N–H) [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

Park,

Cho,

Lee

et al. 2023

Foods

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This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000–1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.

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Section: Resultsmentioning

confidence: 99%

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

Park,

Cho,

Lee

et al. 2023

Foods

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“…Various methodologies have been suggested for qualitative and quantitative assessment of capsaicinoids. These methodologies include approaches that rely on the physical characteristics of molecules, such as visible optical absorption, electrical conductivity, high-performance liquid chromatography (HPLC), surface-enhanced Raman spectroscopy (SERS), near-infrared (IR), time-resolved fluorescence, terahertz, fluorescence immunochromatographic, enzyme-linked immunosorbent assay (ELISA), visible, luminescence, gas chromatography (GC), and the combination of above techniques. , In recent years, there has been significant progress in the development of various device designs aimed at medical diagnostic applications. The task of establishing a direct connection between an electronic device and a biological environment is a major obstacle due to the inherent difficulties in converting biological information into an electrical signal.…”

Section: Introductionmentioning

confidence: 99%

Electronic Tongue Based on ZnO/ITO@glass for Electrochemical Monitoring of Spiciness Levels

Ahmed,

Ansari,

Bishwanathan

et al. 2024

Langmuir

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Capsaicin, a chemical compound present in chili peppers, is widely acknowledged as the main contributor to the spicy and hot sensations encountered during consumption. Elevated levels of capsaicin can result in meals being excessively spicy, potentially leading to health issues, such as skin burning, irritation, increased heart rate and circulation, and discomfort in the gastrointestinal system and even inducing nausea or diarrhea. The level of spiciness that individuals can tolerate may vary, so what may be considered incredibly hot for one person could be mild for another. To ensure food safety, human healthcare, regulatory compliance, and quality control in spicy food products, capsaicin levels must be measured. For these purposes, a reliable and stable sensor is required to quantify the capsaicin level. To leverage the effect of zinc oxide (ZnO), herein, we demonstrated the one-step fabrication process of an electronic tongue (E-Tongue) based on an electrochemical biosensor for the determination of capsaicin. ZnO was electrodeposited on the indium tin oxide (ITO) surface. The biosensor demonstrated the two notable linear ranges from 0.01 to 50 μM and from 50 to 500 μM with a limit of detection (LOD) of 2.1 nM. The present study also included the analysis of real samples, such as green chilis, red chili powder, and dried red chilis, to evaluate their spiciness levels. Furthermore, the E-Tongue exhibited notable degrees of sensitivity, selectivity, and long-term stability for a duration of more than a month. The development of an E-Tongue for capsaicin real-time monitoring as a point-of-care (POC) device has the potential to impact various industries and improve safety, product quality, and healthcare outcomes.

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Nondestructive detection of pungent and numbing compounds in spicy hotpot seasoning with hyperspectral imaging and machine learning

Zhang,

Chen,

Lin

et al. 2025

Food Chemistry

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A feasibility quantification study of capsaicin content in chili powder for rapid evaluation using near-infrared reflectance spectroscopy

Cited by 5 publications

References 30 publications

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging

Electronic Tongue Based on ZnO/ITO@glass for Electrochemical Monitoring of Spiciness Levels

Nondestructive detection of pungent and numbing compounds in spicy hotpot seasoning with hyperspectral imaging and machine learning

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