# Neural Network-Based Analysis and Its Application to Spectroscopy for Mango
## 1 Introduction  
Crop-derived sugar is a significant organic energy resource that is studied in the Earth sciences, playing a crucial role as a primary energy source [1; 2]. Near-infrared spectroscopy, recognized for its efficiency, non-destructiveness, and environmental friendliness, has been extensively used in rapid fruit testing [3]. This technology enables the continuous monitoring of brix levels in fruits without altering their visual appearance, offering essential support and analysis for fruit research, development, cultivation, ripeness assessment, harvest timing, storage, transportation, distribution, and pricing [4; 5; 6]. While NIR spectroscopy effectively captures essential fruit characteristics, its modeling process using Fourier transform NIR spectroscopy can be impacted by irrelevant variables, leading to model interference, weak predictive ability, and poor stability.  
In recent years, extensive research efforts have focused on developing models for detecting fructose content. [PERSON] et al. utilized the absorption method to assess whole apples, measuring transmitted light in the 650-950 nm wavelength range to analyze absorption levels and predict dry matter content, and ultimately achieved favorable results [4]. In 2013, [PERSON] et al. conducted predictive modeling of wave bandwidth in two specific ranges, revealing that the modeling effectiveness was optimal within the wavelength range of 1100-2350 nm [7]. [PERSON] et al. conducted investigations on apple brix degree modelsat varying measuring distances. [PERSON] et al. employed a Charge-Coupled Device (CCD) spectrometer for mango brix content detection, integrating spectrum pretreatment to establish a reliable prediction model. [PERSON] et al. devised a multispectral imaging system for evaluating total soluble solids (TSS), titratable acidity (TA), and pH levels in pomegranates [8]. [PERSON] et al. employed a neural network algorithm to enhance the predictive model for estimating the brix content of pear fruits, assessing the impact of uncorrelated factors on the test samples [9]. [PERSON] et al. utilized near-infrared (NIR) spectroscopy to detect sugar and acidity ratios in apples, developing a relationship model between spectra and sugar and acidity levels using the partial least squares model. Their findings demonstrate that this NIR rapid detection method enables non-destructive assessment of the internal quality of apples [10]. While advancements have been achieved in NIR spectral analysis techniques and related research findings, challenges persist in addressing issues such as local miniaturization and inconsistent weight assignment in the BP neural network, hindering the effective resolution of measurement challenges in practical applications.  
In this investigation, our research team collected spectral data and recorded sugar content from various mango varieties within a controlled experimental setting, utilizing primary equipment including the Ocean Optics NIR-Quest spectrometer. The acquired spectral data underwent preprocessing techniques including Standard Normal Variate (SNV) processing, Savitzky-Golay (S-G) smoothing, outlier removal, and interpolation. A brick model was developed for the processed data using a partial least squares approach integrated with the BP neural network algorithm, which was further optimized through a simulated annealing algorithm to enable the non-destructive prediction of mango brix levels. This methodology holds significance for enhancing quality control measures in agricultural production and processing [11].