Near-infrared spectrometric techniques for nondestructive quality evaluation of peaches, tangerines and processing tomatoes

Quality assurance and evaluation of fruits and vegetables for the fresh market and processing industries is becoming a fundamental requirement due to increased quality consciousness of consumers. Though many products have well established quality grade standards based on external attributes which are evaluated using objective or subjective methods, final acceptance of products by consumers is strongly influenced by internal quality traits. Nondestructive quality evaluation techniques are needed to evaluate and sort products into different grades based on internal quality attributes. Among the various methods available, near infrared (NIR) spectroscopic techniques have shown promise as a rapid and nondestructive tool to evaluate various internal quality attributes such as soluble solids content (SSC), dry matter or moisture content, and the presence of internal defects in fruits and vegetables. Experiments were conducted to develop nondestructive NIR spectrometric techniques for the measurement of SSC of peach and processing tomato and to detect the internal disorder, section drying, in tangerine. Variability of SSC or dry matter content within individual units of selected fruits and vegetables was studied and the significance of such variability to the development and performance of NIR calibration equations were discussed. Multiple linear regression calibration equations developed using four cultivars of peach over three seasons, had a multiple regression coefficient (R) of 0.94 and a standard error of calibration (SEC) of 0.79% and could predict SSC in independent populations with standard error of prediction (SEP) of 1.11%, bias of -0.24% and simple correlation coefficient (r) between the laboratory estimated and NIR predicted SSC of 0.85. Likewise, a neural network calibration equation developed for processing tomato had a R of 0.80 with a SEC of 0.42% and predicted the SSC of unprocessed whole fruit with a SEP = 0.52%, bias = -0.03% and r = 0.69. These results demonstrated that the NIR techniques developed for peach and tomato could be employed to sort fruits into two or three grades with relatively little error. Experiments with tangerine showed distinctive differences in NIR optical properties of healthy juice vesicles and those with section drying. These differences were exploited to nondestructively evaluate the presence of the internal section drying disorder in intact fruit. Information gained on within unit constituent variability of fruits and vegetables was used to optimize the accuracy of NIR calibration equations. Feasible applications of these methods include packing-house sorting of peach for sweetness and tangerine for the presence of section drying, rapid quality assessment of tomato batches for processing and quality assessment of these fruits in breeding programs and research studies in which quality is an important component.