The growing global demand for fresh fruits necessitates efficient, non-destructive methods for assessing fruit quality, especially for export. Traditional fruit quality assessment techniques are often labor-intensive, time consuming, and destructive, making them unsuitable for large-scale or real-time analysis. To address these limitations, this study provides a comprehensive bibliometric analysis of near infrared spectroscopy (NIR spectroscopy) in fruit analysis. The study adhered to the PRISMA guidelines to extract peer-reviewed papers from 2003 to 2023 from the Scopus database. Thereafter, the bibliometric analysis was conducted using R software’s Bibliometrix package to evaluate global trends, key contributors, and emerging themes in the field. The results show that NIR spectroscopy has become an essential tool for non-destructive quality assessment in fruits, accurately predicting attributes such as total acidity, soluble solids content, and internal disorders. Integrating machine learning and artificial intelligence models, particularly artificial neural networks and deep learning, has further enhanced the predictive capabilities of NIR spectroscopy. In addition, technological innovations such as portable spectrometers and hyperspectral imaging have expanded the applicability of NIR spectroscopy beyond laboratory settings to in-field assessments. The findings highlight the ongoing evolution of NIR spectroscopy technology, its significant impact on fruit quality evaluation, and the potential for future advancements in this field. Future research should focus on improving the adaptability of NIR spectroscopy to diverse fruit types and production environments and exploring the use of artificial intelligence and machine learning to further enhance data interpretation and predictive accuracy. Such innovations could significantly broaden the scope of NIR spectroscopy applications, making it a critical tool for sustainable agriculture and global food security.
Lamptey et al. (Sat,) studied this question.