| 000 | 02591nab|a22003857a|4500 | ||
|---|---|---|---|
| 001 | 66498 | ||
| 003 | MX-TxCIM | ||
| 005 | 20241204091649.0 | ||
| 008 | 202312s2023||||mx |||p|op||||00||0|eng|d | ||
| 022 | _a0889-1575 | ||
| 022 | _a1096-0481 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.1016/j.jfca.2023.105708 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 0 |
_aWenfei Tian _926337 |
|
| 245 | 1 | 0 |
_aQuantification of food bioactives by NIR spectroscopy : _bCurrent insights, long-lasting challenges, and future trends |
| 260 |
_bAcademic Press Inc., _c2023. _aUnited States of America : |
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| 500 | _aPeer review | ||
| 520 | _aConventional wet-chemistry methods for quantitative analysis of food bioactives are time-consuming, costly, and generate hazardous waste. Near-infrared (NIR) spectroscopy combined with chemometrics is a rapidly advancing way of quantifying food bioactives. This review presents the key principles and workflow for NIR model development. Sample representativeness, chemometrics methods, proper sample preparations, concentration of the analytes, optical features of the instruments, data distribution and postharvest process are key considerations for enhanced model performance. Additionally, this review critically discusses potential issues that have not been recognized in previous studies. Difficulties in obtaining comprehensive data set, comparison of different chemometric algorithms, data interpretation and method transfer are major challenges for NIR studies. Findable, Accessible, Interoperable and Reusable (FAIR) data management is proposed for future advancements of NIR methods. Theoretical calculation will shed light on result interpretation and calibration transfer. Wider applications of miniaturized NIR systems will benefit the quality control of entire value chain from farm to fork. | ||
| 546 | _aText in English | ||
| 591 | _aWenfei Tian : Not in IRS staff list but CIMMYT Affiliation | ||
| 650 | 4 |
_aAntioxidants _2AGROVOC _911020 |
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| 650 | 7 |
_aInfrared spectrophotometry _2AGROVOC _96179 |
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| 650 | 7 |
_aData Collection _2AGROVOC _99145 |
|
| 650 | 7 |
_aMethods _2AGROVOC _91178 |
|
| 700 | 0 |
_aYonghui Li _926340 |
|
| 700 | 1 |
_aGuzmán, C. _928834 |
|
| 700 | 1 |
_a Ibba, M.I. _8001711897 _95836 _gGlobal Wheat Program |
|
| 700 | 1 |
_aTilley, M. _97487 |
|
| 700 | 0 |
_aDonghai Wang _927495 |
|
| 700 | 1 |
_aHe Zhonghu _8INT2411 _9838 _gGlobal Wheat Program |
|
| 773 | 0 |
_tJournal of Food Composition and Analysis _gv. 124, art. 105708 _dUnited States of America : Academic Press Inc., 2023 _x0889-1575 |
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| 942 |
_cJA _n0 _2ddc |
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| 999 |
_c66498 _d66490 |
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