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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #298319
Title: Evaluation of canning quality traits in black beans (Phaseolus vulgaris L.) by visible/near-infrared spectroscopy

Author
item Mendoza, Fernando
item Cichy, Karen
item Lu, Renfu
item KELLY, JAMES - Michigan State University

Submitted to: Food and Bioprocess Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2014
Publication Date: 3/12/2014
Citation: Mendoza, F., Cichy, K.A., Lu, R., Kelly, J.D. 2014. Evaluation of canning quality traits in black beans (Phaseolus vulgaris L.) by visible/near-infrared spectroscopy. Food and Bioprocess Technology. 7:2666-2678.

Interpretive Summary: Black bean (Phaseolus vulgaris L.) is a nutritionally rich seed legume. Hence, improved consumer acceptance of its processed products will contribute to the human health and nutrition and further increase black bean production opportunities for farmers and processors. Black beans are commonly consumed as a canned product. During canning and processing, they are prone to loss of seed color and firmness and skin breakage. A prior knowledge of these quality traits before canning can, thus, help bean breeders and processors make better decisions. This study evaluated the feasibility of visible and near-infrared (Vis/NIR) spectroscopy for predicting from intact dry beans canning quality traits or attributes (i.e., hydration coefficient, appearance, color, washed drained coefficient, and texture), which are routinely used by bean breeders and processors. Vis/NIR spectral data for the spectral region of 400-2,500 nm were obtained from intact black bean samples harvested in 2010, 2011 and 2012, while the canning quality attributes of canned beans were measured using the standard procedure. Statistical models were then developed relating spectral data to canning quality attributes. Vis/NIR spectroscopy appeared promising for predicting hydration coefficient, washed drained coefficient and visual color ratings with the correlation coefficient being as high as 0.810. However, prediction accuracies for visual appeal and texture of canned beans were still low. Further, statistical categorical models was able to grade the bean samples into two sensory quality categories (i.e., ‘acceptable’ and ‘unacceptable’) with average classification accuracies of 72.6% or higher. This research showed that statistical modeling of sensorial preferences for appearance and color using Vis/NIR data has the potential to predict consumer preferences of canned black beans.

Technical Abstract: Black bean (Phaseolus vulgaris L.) processing presents unique challenges because of discoloration, breakage, development of undesirable textures and off-flavors during canning and storage. These quality issues strongly affect processing standards and consumer acceptance for beans. In this research, visible and near-infrared reflectance (Vis/NIR) data for the spectral region of 400-2,500 nm were acquired from intact dry beans for predicting five canning quality traits, i.e., hydration coefficient (HC), visual appearance (APP) and color (COL), washed drained coefficient (WDC), and texture (TXT). A total of 471 bean samples harvested and canned in 2010, 2011 and 2012 were used for analysis. Partial least squares regression (PLSR) models were developed from the Vis/NIR data to predict the canning quality attributes. The PLSR models showed low predictive performance, as measured by correlation coefficient for prediction (R), for APP (R = 0.275-0.566) and TXT (R = 0.270-0.681), but promising results for predicting HC (R = 0.517-0.810), WDC (R = 0.420-0.796) and COL (R = 0.533-0.758). In comparison, color measurements from a colorimeter on drained canned beans showed consistently good predictions for COL (R = 0.796-0.907). In spite of the low or relatively poor agreement among the sensory panelists as determined by multirater Kappa analysis (K of 0.20 for APP and 0.18 for COL), a linear discriminant model using the Vis/NIR data was able to classify the canned bean samples into two sensory quality categories of 'acceptable' and 'unacceptable', based on panelists’ ratings for APP and COL traits of canning beans, with classification accuracies of 72.6% or higher. While Vis/NIR technique is promising for assessing bean canning quality from intact dry beans, improvements in the instrumentation are needed in order to meet the application requirements.