Use of an NIR MEMS spectrophotometer and visible/NIR hyperspectral imaging systems to predict quality parameters of treated ground peppercorns

Esquerre, Carlos; Achata, Eva M.; García‐Vaquero, Marco; Zhang, Zhihang; Tiwari, Brijesh K.; O’Donnell, Colm P.

doi:10.1016/j.lwt.2020.109761

Cited by 16 publications

(5 citation statements)

References 39 publications

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“…In the whole extraction process, there is no association with the dependent variable, and completely independent of the dependent variable, so the extraction process is simple. However, the concept of extracting components in PLSR analysis is to derive a few components from independent variables, so as to only better conclude the data of the original independent variables and have a strong ability to interpret the dependent variables and are not correlated with each other [39]. It adopts a circular data decomposition and extraction approach, and the process is much more complex than principal component extraction.…”

Section: Prediction Of Dietary Ber Content In Bamboo Shootsmentioning

confidence: 99%

Predicting the dietary fiber content of fresh-cut bamboo shoots using a visible and near-infrared hyperspectral technique

Xie

Xiang

et al. 2023

Food Measure

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The dietary ber content in fresh-cut bamboo shoots is considered crucial for the quality of processed bamboo shoots products. This study aimed to explore the potential of applying two different hyperspectral techniques, namely visible near infrared (Vis-NIR) spectroscopy and near infrared (NIR) in the quick and non-destructive prediction of the dietary ber content of fresh-cut bamboo shoots. The Vis-NIR and NIR hyperspectral data were collected to establish partial least square regression (PLSR) and principal component regression (PCR) calibration model for the average spectrum of fresh-cut bamboo shoots and their corresponding dietary ber content. Subsequently, data fusion analysis, various preprocessing methods, and principal component analysis (PCA) were used to optimize the model. The results indicated that superior models were obtained based on low-level fusion data when compared with the corresponding methods based on single spectral data. The optimal SNV-PCA-PLSR model achieved a good performance with coe cient of determination of prediction (R 2 p) of 0.902, and root mean square errors of prediction (RMSEP) of 0.135. Therefore, hyperspectral technique combined with data fusion analysis can be a promising approach for non-invasive quality supervision of bamboo shoots products in varied processing states.

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Section: Prediction Of Dietary Ber Content In Bamboo Shootsmentioning

confidence: 99%

Predicting the dietary fiber content of fresh-cut bamboo shoots using a visible and near-infrared hyperspectral technique

Xie

Xiang

et al. 2023

Food Measure

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“…The two pieces of equipment delivered similar performance. In another study [127], a NIRONE (Spectral Engines, Finland), which is an e-type spectrometer (MEMS-based Fabry-Pérot; Figure 2, Table 1), was compared with a hyperspectral imaging system (DV Optics, Padova, Italy) to predict the QA of treated ground peppercorns. Again, the new spectrometer performed as well or better than the classical counterpart.…”

Section: Emerging Technologies For Portable Spectroscopymentioning

confidence: 99%

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

et al. 2022

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As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

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“…Research in the field of fruit detection using NIR spectroscopy mainly focuses on establishing spectral signal models and developing corresponding processing methods. In terms of model building, intelligent algorithms such as principal component analysis (PCA), partial least squares (PLS), and support vector machines (SVM) have been realized to construct static models for the nondestructive testing of fruit quality [ 9 , 10 , 11 , 12 ]. Numerous researchers have conducted studies indicating that the fruit quality may change significantly due to biological variability, which affects light propagation and light–substance interactions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

Guo,

Chen,

Zhang

et al. 2024

Foods

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Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.

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Use of an NIR MEMS spectrophotometer and visible/NIR hyperspectral imaging systems to predict quality parameters of treated ground peppercorns

Cited by 16 publications

References 39 publications

Predicting the dietary fiber content of fresh-cut bamboo shoots using a visible and near-infrared hyperspectral technique

Predicting the dietary fiber content of fresh-cut bamboo shoots using a visible and near-infrared hyperspectral technique

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

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