Visible, near- and mid-infrared spectroscopy coupled with an innovative chemometric strategy to control apple puree quality

Deep learning combined Monte Carlo simulation reveal the fundamental light propagation in apple puree: Monitoring the quality changes from different cultivar, storage period and heating duration

This work explored the light propagation of purees from a large variability of apple cultivar, storage period and heating duration based on their optical absorption (μa) and reduced scattering (μs') properties at 900-1650 nm, in order to better monitor the chemical, structural and rheological parameters. The prolonged heating duration modified intensively on puree structure and rheology, and resulted significant increases of μs' at 900-1350 nm. Based on Monte Carlo simulation, the maximum light attenuation distance at 1050 nm of 'Golden Delicious' and 'Red Delicious' apple puree increased intensively from 16.22 mm to 17.60 mm and from 16.19 mm to 17.41 mm respectively while thermal processing duration from 10 min to 20 min. Back propagation neural network models based on μa and μs' can monitor their dry matter content, titratable acidity, apparent viscosity and viscoelasticity, with the RPD > 2.53. These provided fundamental knowledge on light propagation of puree matrix and the potential strategy to monitor their quality.

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky-Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands

Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.

Detection of Alternaria alternata infection in winter jujubes based on optical properties and their correlation with internal quality parameters during storage

In this work, a single integrating sphere system was applied to characterize the optical absorption (μ_a) and reduced scattering (μ_s') properties (550 - 1050 nm) in winter jujube flesh infected by Alternaria alternata during storage at 4 and 20 °C, respectively. Meanwhile, physical (L*, a*, weight loss) and biochemical characteristics (soluble solids content, titratable acids, chlorophyll, total phenolic, and ascorbic acid) of winter jujubes were measured. Among them, chlorophyll, weight loss and ascorbic acid were highly correlated with μ_a at 680 nm, 690 nm, while chlorophyll and a* had the best correlations with μ_s' at 700 - 920 nm. These optimal optical properties were proved efficiently contributed to the disease detection of winter jujubes after 12 days at 4 °C and 3 days at 20 °C during storage, with satisfactory discrimination accuracies (acc > 93.75 %). Consequently, optical properties in Vis-NIR region were available to detect the postharvest disease in winter jujubes.

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.

Analysis of the chemical composition of natural carbohydrates - An overview of methods

Natural carbohydrates are gaining importance over a wide spectrum of human activity due to their versatile functionalities. The properties of carbohydrates are currently used in many branches of industry and new possibilities of their utilization, like in medicine or materials science, are demonstrated systematically. The attractive properties of carbohydrates result from their chemical structure and ability to form macromolecules and derivatives. Each application of carbohydrate requires a knowledge of their chemical composition, which due to the number and differentiation of monosaccharides and their spatial forms is often challenging. This review presents an overview on sample preparation and the methods used for the determination of the fine chemical structure of natural carbohydrates. Most popular and reliable colorimetric, chromatographic and spectroscopic methods are presented with an emphasis on their pros and cons.

Feasibility study on early identification of freshness decay of fresh-cut kiwifruit during cold chain storage by Fourier transform-near infrared spectroscopy combined with chemometrics

This work mainly aimed to evaluate the feasibility of Fourier transform-near infrared spectroscopy (FT-NIRS) combined with chemometrics in early identification of freshness decay of fresh-cut kiwifruit during simulated cold chain storage, with organoleptic evaluation as a reference. By linear discriminant analysis, the freshness decay could be identified after only 2 days of cold storage, corresponding to freshness level of 3.41 ± 0.27 N (hardness), 0.70 ± 0.05 g/kg (total acid), 8.62 ± 0.06 g/100 g (reducing sugars), 62.04 ± 1.03 mg/100 g (vitamin C) and 2.05 ± 0.11 log₁₀ CFU/g (total plate count). Organoleptic evaluators could not perceive the freshness decay that occurred after 2 days of cold storage. Moreover, the freshness decay could be well quantitatively predicted by partial least squares regression, with low RMSE_p (0.18-05.42) and high R² (0.90-0.96). FT-NIRS appears to be a promising option for early warning of the freshness decay of fresh-cut kiwifruit during cold chain storage, thereby preventing serious spoilage and ensuring fresh fruits for consumers. PRACTICAL APPLICATION: This work is based on the fact that fresh-cut kiwifruit is prone to freshness decay under unstable cold chain conditions, using FT-NIRS combined with chemometrics to identify the freshness decay early and rapidly, to a certain extent, early warn freshness decay and effectively prevent serious spoilage. The technology can be used for food regulatory agencies to monitor the freshness of fresh-cut kiwifruit, and can also be applied for fruit processing enterprises and dealers to ensure the freshness and high quality of fresh-cut kiwifruit.

Nutraceutical Chewing Candy Formulations Based on Acetic, Alcoholic, and Lactofermented Apple Juice Products

The aim of this study was to develop nutraceutical chewing candy (NCC) formulations based on acetic, alcoholic, and lactofermented apple juice (AJ) products. In addition, different texture-forming (gelatin, pectin) and sweetening (stevia, xylitol) agents were tested. To implement the aim of this study, combinations based on AJ, prepared from fresh and frozen apples, apple cider (C) samples (No.1, No.2, No.3, and No.4), and apple vinegar (V) were used. First, the most appropriate combination was selected by evaluating overall acceptability (OA) and emotions induced for consumers (EIC). In addition, the volatile compound (VC) profile, and physicochemical and antimicrobial characteristics of the developed combinations were analyzed. For AJ fermentation, lactic acid bacteria (LAB) strains possessing antimicrobial properties (LUHS122—L. plantarum and LUHS210—L. casei) were used. AJ prepared from frozen apples had 11.1% higher OA and 45.9%, 50.4%, and 33.3% higher fructose, glucose, and saccharose concentrations, respectively. All the tested C samples inhibited Bacillus subtilis and had an average OA of 6.6 points. Very strong positive correlations were found between AJ and C OA and the emotion ‘happy’; comparing lactofermented AJ, the highest OA was obtained for AJ fermented for 48 h with LUHS122, and a moderate positive correlation was found between AJ OA and the emotion ‘happy’ (r = 0.7617). This sample also showed the highest viable LAB count (7.59 log₁₀ CFU mL⁻¹) and the broadest spectrum of pathogen inhibition (inhibited 6 out of 10 tested pathogens). Further, acetic, alcoholic, and lactofermented AJ product combinations were tested. For the preparation of NCC, the combination consisting of 50 mL of AJ fermented with LUHS122 for 48 h + 50 mL C-No.3 + 2 mL V was selected because it showed the highest OA, induced a high intensity of the emotion ‘happy’ for the judges, and inhibited 8 out of 10 tested pathogens. Finally, the OA of the prepared NCC was, on average, 9.03 points. The combination of acetic, alcoholic, and lactofermented AJ products leads to the formation of a specific VC profile and increases the OA and antimicrobial activity of the products which could be successfully applied in the food and nutraceutical industries.

Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy

Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R2c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R2p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.

Rapid detection of Chinese-specific peony seed oil by using confocal Raman spectroscopy and chemometrics

Peony seed oil (PSO) is a new woody nut oil which is unique to China. Its unsaturated fatty acids are over 90% and are rich in α - linolenic acid. Although the PSO industry is in its infancy, it is bound to become a top vegetable oil food material because of its own advantages. The potential high commercial profit of its adulteration with cheap vegetable oil will be an important factor hindering the healthy development of PSO industry. It is of great significance to study the adulteration of PSO for preventing large-scale adulteration. In this study, the qualitative and quantitative analysis of PSO was realised based on Raman spectroscopy combined with chemometrics analysis, and the fatty acid composition of PSO was analysed according to Raman characteristic peaks. The technology can be applied to routine analysis and quality control of PSO.

Mid-infrared technique to forecast cooked puree properties from raw apples: A potential strategy towards sustainability and precision processing

The potential of MIRS was investigated to: i) differentiate cooked purees issued from different apples and process conditions, and ii) predict the puree quality characteristics from the spectra of homogenized raw apples. Partial least squares (PLS) regression was tested both, on the real spectra of cooked purees and their reconstructed spectra calculated from the spectra of homogenized raw apples by direct standardization. The cooked purees were well-classified according to apple thinning practices and cold storage durations, and to different heating and grinding conditions. PLS models using the spectra of homogenized raw apples can anticipate the titratable acidity (the residual predictive deviation (RPD) = 2.9), soluble solid content (RPD = 2.8), particle averaged size (RPD = 2.6) and viscosity (RPD ≥ 2.5) of cooked purees. MIR technique can provide sustainable evaluations of puree quality, and even forecast texture and taste of purees based on the prior information of raw materials.

Apple Fermented Products: An Overview of Technology, Properties and Health Effects

As an easily adapted culture, with overloaded production in some parts of the globe, apples and their by-products are being redirected to pharmaceutical, canning and beverages industries, both alcoholic and non-alcoholic. Fermentation is generally considered to increase the bioavailability of bioactive compounds found in apple, by impacting, through a high degree of changes, the product’s properties, including composition and health-promoting attributes, as well as their sensory profile. Probiotic apple beverages and apple vinegar are generally considered as safe and healthy products by the consumers. Recently, contributions to human health, both in vivo and in vitro studies, of non-alcoholic fermented apple-based products have been described. This review highlighted the advances in the process optimization of apple-based products considering vinegar, cider, pomace, probiotic beverages and spirits’ technologies. The different processing impacts on physical-chemical, nutritional and sensory profiles of these products are also presented. Additionally, the harmful effects of toxic compounds and strategies to limit their content in cider and apple spirits are illustrated. New trends of fermented apple-based products applicability in tangential industries are summarized.