Near-Infrared Spectroscopy and Aquaphotomics for Monitoring Mung Bean (
chemometrics
germination
malting
water spectral pattern
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
17 Jan 2021
17 Jan 2021
Historique:
received:
07
12
2020
revised:
02
01
2021
accepted:
15
01
2021
entrez:
22
1
2021
pubmed:
23
1
2021
medline:
15
4
2021
Statut:
epublish
Résumé
Mung bean is a leguminous crop with specific trait in its diet, namely in the form of anti-nutrient components. The sprouting process is commonly done for better nutritional acceptance of mung bean as it presents better nutritional benefits. Sprouted mung bean serves as a cheap source of protein and ascorbic acid, which are dependent on the sprouting process, hence the importance of following the biological process. In larger production scale, there has not been a definite standard for mung bean sprouting, raising the need for quick and effective mung bean sprout quality checks. In this regard, near-infrared spectroscopy (NIRS) has been recognized as a highly sensitive technique for quality control that seems suitable for this study. The aim of this paper was to describe quality parameters (water content, pH, conductivity, and ascorbic acid by titration) during sprouting using conventional analytical methods and advanced NIRS techniques as correlative methods for modelling sprouted mung beans' quality and ascorbic acid content. Mung beans were sprouted in 6 h intervals up to 120 h and analyzed using conventional methods and a NIR instrument. The results of the standard analytical methods were analyzed with univariate statistics (analysis of variance (ANOVA)), and the NIRS spectral data was assessed with the chemometrics approach (principal component analysis (PCA), discriminant analysis (DA), and partial least squares regression (PLSR)). Water content showed a monotonous increase during the 120 h of sprouting. The change in pH and conductivity did not describe a clear pattern during the sprouting, confirming the complexity of the biological process. Spectral data-based discriminant analysis was able to distinctly classify the bean sprouts with 100% prediction accuracy. A NIRS-based model for ascorbic acid determination was made using standard ascorbic acid to quantify the components in the bean extract. A rapid detection technique within sub-percent level was developed for mung bean ascorbic acid content with R
Identifiants
pubmed: 33477304
pii: s21020611
doi: 10.3390/s21020611
pmc: PMC7830487
pii:
doi:
Substances chimiques
Ascorbic Acid
PQ6CK8PD0R
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Ministry for Innovation and Technology
ID : ÚNKP-20-3-II-SZIE-12
Organisme : European Social Fund
ID : EFOP-3.6.3-VEKOP-16-2017-00005
Organisme : Ministry for Innovation and Technology
ID : ÚNKP-20-3-II-SZIE-25
Organisme : Ministry for Innovation and Technology
ID : TKP2020-IKA-12
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