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Optimization of sweet basil harvest time and cultivar characterization using near-infrared spectroscopy, liquid and gas chromatography, and chemometric statistical methods
Year:
2021
Authors :
Chalupowicz, Daniel
;
.
Fallik, Elazar
;
.
Hen, Yaira
;
.
Kenigsbuch, David
;
.
Maurer, Dalia
;
.
Volume :
Co-Authors:

Matan Birenboim,
Daniel Chalupowicz,
Dalia Maurer
Shimon Barel,
Yaira Chen,
Elazar Falik,
David Kengisbuch,
Jakob A Shimshoni

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

BACKGROUND

Terpene, eugenol and polyphenolic contents of basil are major determinants of quality, which is affected by genetics, weather, growing practices, pests and diseases. Here, we aimed to develop a simple predictive analytical method for determining the polyphenol, eugenol and terpene content of the leaves of major Israeli sweet basil cultivars grown hydroponically, as a function of harvest time, through the use of near-infrared (NIR) spectroscopy, liquid/gas chromatography, and chemometric methods. We also wanted to identify the harvest time associated with the highest terpene, eugenol and polyphenol content.

RESULTS

Six different cultivars and four different harvest times were analyzed. Partial least square regression (PLS-R) analysis yielded an accurate, predictive model that explained more than 93% of the population variance for all of the analyzed compounds. The model yielded good/excellent prediction (R2 > 0.90, R2cv and R2pre > 0.80) and very good residual predictive deviation (RPD > 2) for all of the analyzed compounds. Concentrations of rosmarinic acid, eugenol and terpenes increased steadily over the first 3 weeks, peaking in the fourth week in most of the cultivars. Our PLS-discriminant analysis (PLS-DA) model provided accurate harvest classification and prediction as compared to cultivar classification. The sensitivity, specificity and accuracy of harvest classification were larger than 0.82 for all harvest time points, whereas the cultivar classification, resulted in sensitivity values lower than 0.8 in three cultivars.

CONCLUSION

The PLS-R model provided good predictions of rosmarinic acid, eugenol and terpene content. Our NIR coupled with a PLS-DA demonstrated reasonable solution for harvest and cultivar classification. © 2021 Society of Chemical Industry.

Note:
Related Files :
Chromatography, Gas
Chromatography, Liquid
cultivar
Spectroscopy, Near-Infrared
Sweet basil
Show More
Related Content
More details
DOI :
10.1002/jsfa.11679
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
57417
Last updated date:
02/03/2022 17:27
Creation date:
31/12/2021 01:49
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Scientific Publication
Optimization of sweet basil harvest time and cultivar characterization using near-infrared spectroscopy, liquid and gas chromatography, and chemometric statistical methods

Matan Birenboim,
Daniel Chalupowicz,
Dalia Maurer
Shimon Barel,
Yaira Chen,
Elazar Falik,
David Kengisbuch,
Jakob A Shimshoni

Optimization of sweet basil harvest time and cultivar characterization using near-infrared spectroscopy, liquid and gas chromatography, and chemometric statistical methods

BACKGROUND

Terpene, eugenol and polyphenolic contents of basil are major determinants of quality, which is affected by genetics, weather, growing practices, pests and diseases. Here, we aimed to develop a simple predictive analytical method for determining the polyphenol, eugenol and terpene content of the leaves of major Israeli sweet basil cultivars grown hydroponically, as a function of harvest time, through the use of near-infrared (NIR) spectroscopy, liquid/gas chromatography, and chemometric methods. We also wanted to identify the harvest time associated with the highest terpene, eugenol and polyphenol content.

RESULTS

Six different cultivars and four different harvest times were analyzed. Partial least square regression (PLS-R) analysis yielded an accurate, predictive model that explained more than 93% of the population variance for all of the analyzed compounds. The model yielded good/excellent prediction (R2 > 0.90, R2cv and R2pre > 0.80) and very good residual predictive deviation (RPD > 2) for all of the analyzed compounds. Concentrations of rosmarinic acid, eugenol and terpenes increased steadily over the first 3 weeks, peaking in the fourth week in most of the cultivars. Our PLS-discriminant analysis (PLS-DA) model provided accurate harvest classification and prediction as compared to cultivar classification. The sensitivity, specificity and accuracy of harvest classification were larger than 0.82 for all harvest time points, whereas the cultivar classification, resulted in sensitivity values lower than 0.8 in three cultivars.

CONCLUSION

The PLS-R model provided good predictions of rosmarinic acid, eugenol and terpene content. Our NIR coupled with a PLS-DA demonstrated reasonable solution for harvest and cultivar classification. © 2021 Society of Chemical Industry.

Scientific Publication
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