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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #349407

Research Project: Genetic Enhancement of Dry Bean Nutritional and Processing Qualities

Location: Sugarbeet and Bean Research

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

Author
item MENDOZA, FERNANDO - Michigan State University
item WIESINGER, JASON - Michigan State University
item Lu, Renfu
item NCHIMBI, SUSAN - Sokoine University Of Agriculture
item Miklas, Phillip - Phil
item KELLY, JAMES - Michigan State University
item Cichy, Karen

Submitted to: The Plant Phenome Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/2018
Publication Date: 10/25/2018
Citation: Mendoza, F., Wiesinger, J., Lu, R., Nchimbi, S., Miklas, P.N., Kelly, J.D., Cichy, K.A. 2018. Prediction of cooking time for soaked and unsoaked dry beans (Phaseolus vulgaris L.) using hyperspectral imaging technology. The Plant Phenome Journal. https://doi.org/10.2135/tppj2018.01.0001.
DOI: https://doi.org/10.2135/tppj2018.01.0001

Interpretive Summary: The cooking time of dry beans is an important characteristic that influences consumer consumption patterns. There is wide genetic variability for cooking time. However bean breeders have been reluctant to incorporate cooking time evaluation into their breeding programs because the currently available assessment methods are low-throughput. The objective of this study was to evaluate the performance of hyperspectral imaging (HYPERS) technology for predicting cooking time non-destructively on dry beans. Fourteen dry bean (Phaseolus vulgaris L.) genotypes from five market classes and with a wide range of cooking times were grown in five environments over two years. Cooking time was measured on pre-soaked and unsoaked bean seeds. Hyperspectral images were taken from whole dry seeds and partial least squares regression models based on the extracted hyperspectral image features were developed to predict 1) water uptake, 2) cooking time of soaked beans, and 3) cooking time of unsoaked beans. Relatively good predictions of water uptake were obtained, as measured by the correlation coefficient for prediction (R_pred = 0.789) and standard error of prediction (SEP = 4.4%). Good predictions of cooking time, which ranged between 20 – 160 min for soaked beans, were achieved with R_pred = 0.886 and SEP = 7.9 min. The prediction models for the cooking time of unsoaked beans (ranging between 80 – 396 min) were less robust and accurate (R_pred = 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.

Technical Abstract: The cooking time of dry beans varies widely by genotype and is also influenced by the growing environment, storage conditions, and cooking method. Since this trait is influenced by many factors and dynamic during post-harvest storage, high throughput phenotyping methods to assess cooking time would be useful to breeders interested in developing cultivars with desired cooking time and for food processors looking to optimize operations. The objective of this study was to evaluate the performance of hyperspectral imaging (HYPERS) technology for predicting dry bean cooking time. Fourteen dry bean (Phaseolus vulgaris L.) genotypes from five market classes and with a wide range of cooking times were grown in five environments over two years. Cooking time was measured as the time required for 80% of 25 stainless steel piercing rods to pass through pre-soaked or unsoaked bean seeds. Hyperspectral images were taken from whole dry seeds and partial least squares regression models based on the extracted hyperspectral image features were developed to predict 1) water uptake, 2) cooking time of soaked beans, and 3) cooking time of unsoaked beans. Relatively good predictions of water uptake were obtained, as measured by the correlation coefficient for prediction (R_pred = 0.789) and standard error of prediction (SEP = 4.4%). Good predictions of cooking time, which ranged between 20 – 160 min for soaked beans, were achieved with R_pred = 0.886 and SEP = 7.9 min. The prediction models for the cooking time of unsoaked beans (ranging between 80 – 396 min) were less robust and accurate (R_pred = 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. Due to the genotypic and phenotypic variability of water absorption and cooking time in dry beans, 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.