Data from: Prediction of cooking time for soaked and unsoaked dry beans (Phaseolus vulgaris L.) using hyperspectral imaging technology

The cooking time of dry beans varies widely by genotype and is also
influenced by the growing environment, storage conditions and cooking
method. Thus, high throughput phenotyping methods to assess cooking time
would be useful to breeders interested in developing cultivars with
desired cooking time. The objective of this study was to evaluate the
performance of hyperspectral imaging technology for predicting dry bean
cooking time. Fourteen dry bean (Phaseolus vulgaris L.) genotypes with a
wide range of cooking times were grown in five environments over 2 yr.
Hyperspectral images were taken from whole dry seeds and partial least
squares regression models based on the extracted spectral image features
were developed to predict water uptake and cooking time of both soaked and
unsoaked beans. Relatively good predictions of water uptake were obtained,
as measured by the correlation coefficient for prediction (Rpred=0.789)
and standard error of prediction (SEP=4.4%). Good predictions of cooking
time for soaked beans (ranging between 19.9–95.5 min) were achieved giving
Rpred=0.886 and SEP=7.9 min. The prediction models for the cooking time of
unsoaked beans (ranging between 80–147 min) were less robust and accurate
(Rpred=0.708, SEP=10.6 min). This study demonstrated that hyperspectral
imaging technology has potential for providing a nondestructive, simple,
fast and economical means for estimating the water uptake and cooking time
of dry beans. Moreover, a totally independent set of 110 similar dry bean
samples confirmed the suitability of the technique for predicting cooking
time of soaked beans after updating the calibration model with 20 of the
new samples, giving Rpred=0.872 and SEP=3.7 min. However, due to the
genotypic and phenotypic variability of dry bean properties, periodical
updates of these prediction models with more samples and new bean
accessions, as well as testing other multivariate prediction methods are
needed for further improving model robustness and generalization.