High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods

Our objective was to develop a rapid technique for the non-invasive profiling and quantification of major tomato carotenoids using handheld Raman spectroscopy combined with pattern recognition techniques. A total of 106 samples with varying carotenoid profiles were provided by the Ohio State Univers...

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Published in:	Sensors (Basel, Switzerland) Vol. 20; no. 13; p. 3723
Main Authors:	Akpolat, Hacer, Barineau, Mark, Jackson, Keith A, Akpolat, Mehmet Z, Francis, David M, Chen, Yu-Ju, Rodriguez-Saona, Luis E
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 03-07-2020 MDPI
Subjects:	artificial neural networks beta Carotene Carotene Carotenoids Carotenoids - analysis chemometrics Chromatography Classification Destructive testing Fruits handheld Raman spectroscopy High performance liquid chromatography High-Throughput Screening Assays Humans Laboratories Lasers Lycopene Lycopersicon esculentum - chemistry Mass spectrometry Methods Nondestructive testing Pattern recognition Phenotype Plant Breeding Prediction models Raman spectra Raman spectroscopy Retention Scientific imaging Solvents Spectrophotometry Spectrum Analysis, Raman tomato carotenoids Tomatoes United States > US artificial neural networks chemometrics tomato carotenoids handheld Raman spectroscopy
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Summary:	Our objective was to develop a rapid technique for the non-invasive profiling and quantification of major tomato carotenoids using handheld Raman spectroscopy combined with pattern recognition techniques. A total of 106 samples with varying carotenoid profiles were provided by the Ohio State University Tomato Breeding and Genetics program and Lipman Family Farms (Naples, FL, USA). Non-destructive measurement from the surface of tomatoes was performed by a handheld Raman spectrometer equipped with a 1064 nm excitation laser, and data analysis was performed using soft independent modelling of class analogy (SIMCA)), artificial neural network (ANN), and partial least squares regression (PLSR) for classification and quantification purposes. High-performance liquid chromatography (HPLC) and UV/visible spectrophotometry were used for profiling and quantification of major carotenoids. Seven groups were identified based on their carotenoid profile, and supervised classification by SIMCA and ANN clustered samples with 93% and 100% accuracy based on a validation test data, respectively. All- -lycopene and β-carotene levels were measured with a UV-visible spectrophotometer, and prediction models were developed using PLSR and ANN. Regression models developed with Raman spectra provided excellent prediction performance by ANN (r = 0.9, SEP = 1.1 mg/100 g) and PLSR (r = 0.87, SEP = 2.4 mg/100 g) for non-invasive determination of all- -lycopene in fruits. Although the number of samples were limited for β-carotene quantification, PLSR modeling showed promising results (r = 0.99, SECV = 0.28 mg/100 g). Non-destructive evaluation of tomato carotenoids can be useful for tomato breeders as a simple and rapid tool for developing new varieties with novel profiles and for separating orange varieties with distinct carotenoids (high in β-carotene and high in -lycopene).
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s20133723