Spectrosc. J., Volume 3, Issue 3 (September 2025) – 2 articles

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

Cyanide species pose an environmental concern as they inhibit important biological processes in humans and aquatic systems. There is more focus on free-CN and weak acid dissociables cyanide as hazardous species compared to strong acid dissociables due to their higher reactivity and toxicity. However, the strong acid dissociables cyanide also poses health concerns as it liberates free-CN under ultraviolet irradiation or when present in acidic solutions. Detection of strongly acid dissociables cyanide typically requires its digestion in acidic solutions and measurement of the gaseous HCN produced. A simple infrared spectroscopic method is described here to speciate and quantify three strong acid dissociables cyanide: [Fe(CN)₆]³⁻, [Co(CN)₆]³⁻, and [Au(CN)₂]⁻. The strategy involves precipitating the strongly acid dissociables cyanide using cetyltrimethylethylammonium bromide, capturing the precipitate on a polyethylene membrane, and quantifying the individual strongly acid dissociables cyanide from the IR spectrum recorded in transmission mode through the membrane. Controlling the particle diameter to be in the range of 0.2–2 µm is important. Particles less than 0.2 µm pass through the membrane, whereas particles larger than about 2 µm lead to nonlinearity in quantification. The average %recoveries for [Fe(CN)₆]³⁻, [Co(CN)₆]³⁻, and [Au(CN)₂]⁻ were 100% (%RSD = 7), 91% (%RSD = 7), and 101% (%RSD = 8), respectively. The detection limit for [Fe(CN)₆]³⁻ and [Co(CN)₆]³⁻ were both 20 ppb CN⁻, whereas [Au(CN)₂]⁻ was 100 ppb CN⁻. The detection range was 20–750 ppb CN⁻ for [Fe(CN)₆]³⁻ and [Co(CN)₆]³⁻ and 100–750 ppb CN⁻ for [Au(CN)₂]⁻ with a linear regression of R² = 0.999–1.000. Full article