<![CDATA[High Multispectral imaging has become a promising approach in liquid classification, particularly in distinguishing visually similar but subtly spectrally distinct solutions, such as pure water (H₂O) and water mixed with sodium hydroxide (H₂O with NaOH). This study proposed a classification system based on image segmentation and deep learning, utilizing three leading Convolutional Neural Network (CNN) architectures: ResNet 50, EfficientNetV2, and MobileNetV3. Before classification, each multispectral image was processed through color segmentation in HSV space to highlight the dominant spectral, especially in the hue range of 110 170. The model was trained using a data augmentation scheme and optimized with the Adam algorithm, a batch size of 32, and a sigmoid activation function. The dataset consists of 807 images, including 295 H₂O images and 512 H₂O with NaOH images, which were divided into training (64%), validation (16%), and testing (20%) data. Experimental results show that ResNet50 achieves the highest performance, with an accuracy of 93.83% and an F1 score of 93.67%, particularly in identifying alkaline pollution. EfficientNetV2 achieved the lowest loss (0.2001) and exhibited balanced performance across classes, while MobileNetV3, despite being a lightweight model, remained competitive with a recall of 0.97 in the H₂O with NaOH class. Further evaluation with Grad CAM reveals that all models focus on the most critical spectral areas of the segmentation results. These findings support the effectiveness of combining color-based segmentation and CNN in the spectral classification of liquids. This research is expected to serve as a stepping stone in the development of an efficient and accurate automatic liquid classification system for both laboratory and industrial applications.]]>
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