In situ detection of fruit spoilage based on volatile compounds using the mid-infrared fiber-optic evanescent wave spectroscopy

Comparative Analysis of Chilling Injury in Banana Fruit During Storage: Physicochemical and Microstructural Changes, and Early Optical-Based Nondestructive Identification

Chilling injury (CI) during postharvest storage seriously impairs bananas' quality and marketability. This study systematically investigated CI mechanisms through physicochemical, microstructural, and optical analyses and innovatively developed a hyperspectral imaging (HSI)-based approach for early CI detection. Bananas stored at suboptimal (7 °C) and optimal (13 °C) conditions exhibited distinct physicochemical changes. CI progression was related to increased browning symptoms, an abnormal moisture redistribution (reduced pulp moisture content), and delayed softening. Microstructural analysis revealed membrane destabilization, cellular lysis, intercellular cavity formation, and inhibited starch hydrolysis under chilling stress. Hyperspectral microscope imaging (HMI) captured chilling-induced spectral variations (400-1000 nm), enabling the t-SNE-based clustering of CI-affected tissues. Machine learning models using first derivative (1-st)-processed spectra achieved a high accuracy. Both PLS-DA and RF had a 99% calibration accuracy and 98.5% prediction accuracy for CI classification. Notably, HSI detected spectral signatures of early CI (2 days post-chilling treatment) before visible symptoms, achieving a 100% identification accuracy with an optimized PLS-DA combined with 1-st processing. This study provides a theoretical basis for studying fruit CI mechanisms and a novel nondestructive optical method for early CI monitoring in postharvest supply chains.

Evanescent wave quartz-enhanced photoacoustic spectroscopy employing a side-polished fiber for methane sensing

We present an all-fiber-based laser gas analyzer (LGA) employing quartz-enhanced photoacoustic spectroscopy (QEPAS) and a side-polished fiber (SPF). The LGA comprises a custom quartz tuning fork (QTF) with 0.8 mm prong spacing, two acoustic micro-resonators (mR) located on either side of the prong spacing, and a single-mode fiber containing a 17 mm polished section passing through both mRs and QTF. The SPF polished face is positioned to enable the evanescent wave (EW) to create a photoacoustic wave and excite the fundamental flexural mode of the QTF. Sensor performance was demonstrated using methane in nitrogen gas mixtures, with CH4 mixing ratios ranging from 75 ppmv to 1% (by volume), measured with an accumulation time of 300 ms, and a minimum detection limit of 34 ppmv subsequently determined. The EW-QEPAS sensor is ideal for miniaturization, as it does not contain any free-space optics and is suitable for gas sensing in harsh environments and where mobility is required.

Rapid Determination of Different Ripening Stages of Occidental Pears (Pyrus communis L.) by Volatile Organic Compounds Using Proton-Transfer-Reaction Mass Spectrometry (PTR-MS)

Determination of Occidental pear (Pyrus communis) ripening is difficult because the appearance of Occidental pears does not change significantly during the ripening process. Occidental pears at different ripening stages release different volatile organic compounds (VOCs), which can be used to determine fruit ripeness non-destructively and rapidly. In this study, VOCs were detected using proton-transfer-reaction mass spectrometry (PTR-MS). Notably, data were acquired within 1 min. Occidental pears harvested at five separate times were divided into three ripening stages: unripe, ripe, and overripe. The results showed that the composition of VOCs differed depending on the ripening stage. In particular, the concentrations of esters and terpenes significantly increased during the overripe stage. Three ripening stages were clearly discriminated by heatmap clustering and principal component analysis (PCA). This study provided a rapid and non-destructive method to evaluate the ripening stages of Occidental pears. The result can help fruit farmers to decide the optimum harvest time and hence reduce their economic losses.