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Background: Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and non-destructively to substitute the traditional analytical method.
Results: Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and High-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate, were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05-0.07, 0.04-0.06, 0.82-0.92, and 2.3-3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in good determination of gossypol content in intact cottonseeds.
Conclusions: Near infrared spectroscopy coupled with different spectral pretreatments and PLS regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and non-destructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.
Keywords
intact cottonseed, chemometrics, gossypol, near infrared spectroscopy
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CURRENT STATUS: Under Review
Version 2
Posted 15 Jan, 2021
No community comments so far
Editorial decision: Minor revision
On 06 Mar, 2021
Review #2 received
Received 03 Mar, 2021
Reviewer #2 agreed
On 24 Feb, 2021
Reviewer #1 agreed
On 11 Jan, 2021
Review #1 received
Received 11 Jan, 2021
Reviewers invited
Invitations sent on 07 Jan, 2021
Editor assigned
On 06 Jan, 2021
Submission checks complete
On 06 Jan, 2021
Editor invited
On 06 Jan, 2021
No comments provided
Editorial decision: Major revision
On 11 Nov, 2020
Review #3 received
Received 31 Oct, 2020
Reviewer #3 agreed
On 18 Oct, 2020
Review #1 received
Received 08 Sep, 2020
Review #2 received
Received 07 Sep, 2020
Reviewer #2 agreed
On 26 Aug, 2020
Reviewer #1 agreed
On 23 Aug, 2020
Reviewers invited
Invitations sent on 05 Aug, 2020
First submitted
On 04 Aug, 2020
Editor assigned
On 04 Aug, 2020
Submission checks complete
On 03 Aug, 2020
Editor invited
On 03 Aug, 2020
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Subject Areas
Agricultural Economics and Policy
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This preprint is under consideration at Journal of Cotton Research. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 2
Posted 15 Jan, 2021
No community comments so far
Editorial decision: Minor revision
On 06 Mar, 2021
Review #2 received
Received 03 Mar, 2021
Reviewer #2 agreed
On 24 Feb, 2021
Reviewer #1 agreed
On 11 Jan, 2021
Review #1 received
Received 11 Jan, 2021
Reviewers invited
Invitations sent on 07 Jan, 2021
Editor assigned
On 06 Jan, 2021
Submission checks complete
On 06 Jan, 2021
Editor invited
On 06 Jan, 2021
No comments provided
Editorial decision: Major revision
On 11 Nov, 2020
Review #3 received
Received 31 Oct, 2020
Reviewer #3 agreed
On 18 Oct, 2020
Review #1 received
Received 08 Sep, 2020
Review #2 received
Received 07 Sep, 2020
Reviewer #2 agreed
On 26 Aug, 2020
Reviewer #1 agreed
On 23 Aug, 2020
Reviewers invited
Invitations sent on 05 Aug, 2020
First submitted
On 04 Aug, 2020
Editor assigned
On 04 Aug, 2020
Submission checks complete
On 03 Aug, 2020
Editor invited
On 03 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Agricultural Economics and Policy
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and non-destructively to substitute the traditional analytical method.
Results: Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and High-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate, were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05-0.07, 0.04-0.06, 0.82-0.92, and 2.3-3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in good determination of gossypol content in intact cottonseeds.
Conclusions: Near infrared spectroscopy coupled with different spectral pretreatments and PLS regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and non-destructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.
Figure 1
Figure 2
Figure 3
Figure 4
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