Detection of Internal Browning Disorder in Greensis' Pears Using a Portable Non-Destructive Instrument

Internal browning caused by prolonged cold storage poses a significant challenge to the visual appearance and flavor of Asian pears, which are economically valuable and a primary fruit exported from Korea. To address this issue, we established a cost-effective portable non-destructive piece of testing instrument using visible and near-infrared spectroscopy, focusing on the detection and discrimination of internal browning in ' Greensis ' pears. Our investigation underscores the challenge of visually confirming browning, necessitating alternative methods for accurate assessment. Through comprehensive analysis involving three to four segments of 32 ' Greensis ' pears, a robust calibration equation was derived. By employing partial least square regression on the absorption spectra within a 650-950 nm range, we developed a predictive model for detecting and quantifying browning. Through principal component analysis, normal pears were distinctly segregated from those exhibiting browning symptoms (discrimination accuracy of 95%). Furthermore, we established that pears with a browning index of 25 +/- 2.0 are highly susceptible to browning following extended cold storage. Consequently, our proposed portable non-destructive instrument serves as a pivotal tool for farmers and fruit distributors, enabling efficient and precise selection of high-quality pears in an instance. Overall, our study introduces a practical solution to a pressing issue in the Asian pear industry.

Automated summary

A non-destructive testing instrument was developed using visible and near-infrared spectroscopy to detect and differentiate internal browning in 'Greensis' pears. A robust calibration equation was derived through comprehensive analysis, and a predictive model for detecting and quantifying browning was established. The instrument achieved a discrimination accuracy of 95% between normal pears and those exhibiting browning symptoms through principal component analysis.