Quantitative analysis of Chinese steamed bread staling using NIR, MIR, and Raman spectral data fusion

An, Huanjiong; Zhai, Chen; Zhang, Fan; Ma, Qianyun; Sun, Jianfeng; Tang, Yi‐Wei; Wang, Wenxiu

doi:10.1016/j.foodchem.2022.134821

Cited by 23 publications

(10 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In glucose identification, NIR-MIR spectroscopy has been shown to improve the accuracy of glucose measurement compared to using NIR or MIR spectroscopy alone. This is because glucose has characteristic absorbance bands in both the NIR and MIR regions, which can be used to improve the accuracy and sensitivity of glucose measurement [ [40] , [41] , [42] , [43] ].…”

Section: Methodsmentioning

confidence: 99%

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

Khadem,

Nemat,

Elliott

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

Khadem,

Nemat,

Elliott

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…The best feature selection method was SPA, where 95 feature variables were filtered, a reduction of 1462 compared to the full wavelength. However, neither feature variable selection nor combining image features led to improved model performance, presumably because some of the useful information was eliminated and the algorithm utilized different information with different efficiency [37].…”

Section: Quantitative Analysis Based On Nirs Data and Image Featuresmentioning

confidence: 99%

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques

Jiang,

Lv,

Zhong

et al. 2023

Foods

View full text Add to dashboard Cite

Atractylodis rhizoma (AR) is an herb and food source with great economic, medicinal, and ecological value. Atractylodes chinensis (DC.) Koidz. (AC) and Atractylodes lancea (Thunb.) DC. (AL) are its two botanical sources. The commercial fraud of AR adulterated with Atractylodes japonica Koidz. ex Kitam (AJ) frequently occurs in pursuit of higher profit. To quickly determine the content of adulteration in AC and AL powder, two spectroscopic techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), were introduced. The partial least squares regression (PLSR) algorithm was selected for predictive modeling of AR adulteration levels. Preprocessing and feature variable extraction were used to optimize the prediction model. Then data and image feature fusions were developed to obtain the best predictive model. The results showed that if only single-spectral techniques were considered, NIRS was more suitable for both tasks than HSI techniques. In addition, by comparing the models built after the data fusion of NIRS and HSI with those built by the single spectrum, we found that the mid-level fusion strategy obtained the best models in both tasks. On this basis, combined with the color-texture features, the prediction ability of the model was further optimized. Among them, for the adulteration level prediction task of AC, the best strategy was combining MLF data (at CARS level) and color-texture features (C-TF), at which time the R2T, RMSET, R2P, and RMSEP were 99.85%, 1.25%, 98.61%, and 5.06%, respectively. For AL, the best approach was combining MLF data (at SPA level) and C-TF, with the highest R2T (99.92%) and R2P (99.00%), as well as the lowest RMSET (1.16%) and RMSEP (2.16%). Therefore, combining data and image features from NIRS and HSI is a potential strategy to predict the adulteration content quickly, non-destructively, and accurately.

show abstract

“…The rapid growth of the global population and economic globalization has led to increasing complexity and diversity in the production, processing, transportation, and sale of food within the global food supply chain, thereby presenting new challenges to the development of the food industry (Wang, Li et al., 2021). Consequently, issues related to food quality and safety, such as variety classification, adulteration identification, and nutrient and sensory quality evaluation, have garnered widespread concern (Ahmmed et al., 2022; An et al., 2023; Shakiba et al., 2022). In response to these challenges, there is an urgent need to establish reliable, simple, and efficient food detection approaches to ensure food quality and safety.…”

Section: Introductionmentioning

confidence: 99%

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Guo,

Wang,

Lin

et al. 2024

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low‐level, mid‐level, and high‐level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra‐to‐spectra, spectra‐to‐machine vision, spectra‐to‐electronic nose, and spectra‐to‐nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real‐world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone‐based multi‐sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

show abstract

Quantitative analysis of Chinese steamed bread staling using NIR, MIR, and Raman spectral data fusion

Cited by 23 publications

References 44 publications

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Contact Info

Product

Resources

About