# What's in Your Fruit Juice?--Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy
## 3 Results and Discussion
### IR Spectral Analysis of the Samples  
The FT-IR spectra of fruit juice samples were collected using a dial path accessory, and a representative absorption spectrum of selected samples is visually depicted in Figure 1. The spectral composition of the juices was prominently dominated by water absorption bands within the ranges of 3600-2900 and 1700-1500 cm\({}^{-1}\)[44; 45]. Given their dominating presence, these regions were omitted during model generation because they saturated the detector signal. This exclusion was particularly driven by the inherent high water content in fruit juices, saturating the signal within the mentioned ranges due to the strong infrared absorptivity of water. The fingerprint region (1550-900 cm\({}^{-1}\)) that contains rich information was used to generate the IR models, and the significant bands in this region are given in Figure 1a,b. The most important bands in the fingerprint region were associated with C-C-H, C-O-H, O-C-H (1470-1150 cm\({}^{-1}\)), C-O, and C-C stretching modes (1150-900 cm\({}^{-1}\)) and O-H vibrations (1020-1060 cm\({}^{-1}\)) [46]. Notably, different types of fruit juices exhibited distinct characteristics in the fingerprint region, as visually represented in Figure 1c. Quantification of Main Quality Attributes in Fruit Juices Using Partial Least Squares Regression (PLSR)  
The PLSR was used to develop models for key quality traits in fruit juices, including soluble solids, sucrose, glucose, fructose, total sugar, titratable acidity (TA), and citric and ascorbic acid content. PLSR analysis was carried out by combining the FT-IR spectral data from the portable infrared sensor with reference data obtained from the traditional analytical equipment. Statistical performances for each quality parameter are presented in Table 2. Discrepancies in sample numbers for specific quality parameters are attributed to the exclusion of outliers identified through high leverage and/or studentized residual criteria, and not all samples contain each individual sugar. Figure 1: (a) Representative raw mid-infrared spectra of fruit juice samples in the wavelength range 4000–700 cm\({}^{-1}\), obtained using a portable FT-IR sensor. (b) The fingerprint region (1550–900 cm\({}^{-1}\)) of spectra. (c) Selected samples of spectra in the fingerprint region representing pomegranate juice (gray line), apple juice (orange line), lemonade (red line), orange juice (blue line), grapefruit juice (pink line), and cranberry juice (green line). a.u. *: Arbitrary units. The PLSR calibration models demonstrated a robust correlation between the measured reference values and the predicted values obtained from the FT-IR sensor, as illustrated in Figure 2a-h. In the process of model generation, two to six latent variables were employed, as given in Table 2, explaining 93.53 to 99.85% of the total variance. The determination of the optimum number of latent variables, crucial for minimizing prediction error [47], was executed through a cross-validation (leave-one-out) procedure, with a focus on fulfilling the minimum standard error of cross-validation (SECV) criteria, which involved plotting the SECV against PLSR factors. In order to generate accurate and robust prediction models, it was crucial to choose the optimal number of latent variables in calibration models. While incorporating more factors enhances prediction accuracy by capturing relevant variation, an excess can introduce random noise and over-fitting. Conversely, using too few factors may result in under-fitting, failing to explain essential data variation. Finding this balance is critical, as under-fitted models exhibit high bias and low variance, while over-fitted models show low bias and high variance [48]. The models for the main quality traits of fruit juice exhibit strong correlations (RCV \(\geq\) 0.93) and low errors (SECV \(\leq\) 59.42 mg/100 g), as detailed in Table 2. Lower SECV values indicate good predictive performance, while RCV values close to 1, especially above 0.8, are considered excellent for accurate predictions [49].