Quantification of Total Phenolic and Carotenoid Content in Blackberries (Rubus Fructicosus L.) Using Near Infrared Spectroscopy (NIRS) and Multivariate Analysis

Improvement of Antioxidant Activity and Sensory Properties of Functional Cookies by Fortification with Ultrasound-Assisted Hot-Air-Drying Blackberry Powders

In response to the global challenge of food wastage and high perishability of blackberries, this study evaluated the use of ultrasound-assisted hot air drying (US-HAD) to convert downgraded blackberries into powders, comparing it with traditional hot air drying (HAD). US-HAD reduced the drying time and achieved a final moisture content of 12%. Physicochemical analyses (colourimetry, total soluble solids, titratable acidity, and total phenolic content) were conducted on fresh fruit, powders, and fortified cookies. US-HAD cookies exhibited promising antioxidant activity, with ABTS values ranging from 8.049 to 8.536 mmol TEAC/100 g and DPPH values from 8.792 to 9.232 mmol TEAC/100 g, significantly higher than control cookies. The TPC was 13.033 mgGAE/g in HAD cookies and 13.882 mgGAE/g in US-HAD cookies. UHPLC-ESI-MS analysis showed an increase in phenolic compounds content in fortified cookies compared to the control. Sensory analysis highlighted a superior blackberry flavour and overall acceptability in US-HAD cookies, with statistical analysis confirming their superior nutritional and sensory qualities. Integrating US-HAD blackberry powder into cookies helps reduce food waste and enhances the nutritional profiles of baked goods, offering functional foods with health benefits. This work provides a scientific basis for developing enriched functional cookies, offering a healthy and sustainable alternative for utilising damaged fruits.

Mandelonitrile lyase MDL2-mediated regulation of seed amygdalin and oil accumulation of Prunus Sibirica

BACKGROUND

The Prunus sibirica seeds with rich oils has great utilization, but contain amygdalin that can be hydrolyzed to release toxic HCN. Thus, how to effectively reduce seed amygdalin content of P. sibirica is an interesting question. Mandelonitrile is known as one key intermediate of amygdalin metabolism, but which mandelonitrile lyase (MDL) family member essential for its dissociation destined to low amygdalin accumulation in P. sibirica seeds still remains enigmatic. An integration of our recent 454 RNA-seq data, amygdalin and mandelonitrile content detection, qRT-PCR analysis and function determination is described as a critical attempt to determine key MDL and to highlight its function in governing mandelonitrile catabolism with low amygdalin accumulation in Prunus sibirica seeds for better developing edible oil and biodiesel in China.

RESULTS

To identify key MDL and to unravel its function in governing seed mandelonitrile catabolism with low amygdalin accumulation in P. sibirica. Global identification of mandelonitrile catabolism-associated MDLs, integrated with the across-accessions/developing stages association of accumulative amount of amygdalin and mandelonitrile with transcriptional level of MDLs was performed on P. sibirica seeds of 5 accessions to determine crucial MDL2 for seed mandelonitrile catabolism of P. sibirica. MDL2 gene was cloned from the seeds of P. sibirica, and yeast eukaryotic expression revealed an ability of MDL2 to specifically catalyze the dissociation of mandelonitrile with the ideal values of Km (0.22 mM) and Vmax (178.57 U/mg). A combination of overexpression and mutation was conducted in Arabidopsis. Overexpression of PsMDL2 decreased seed mandelonitrile content with an increase of oil accumulation, upregulated transcript of mandelonitrile metabolic enzymes and oil synthesis enzymes (involving FA biosynthesis and TAG assembly), but exhibited an opposite situation in mdl2 mutant, revealing a role of PsMDL2-mediated regulation in seed amygdalin and oil biosynthesis. The PsMDL2 gene has shown as key molecular target for bioengineering high seed oil production with low amygdalin in oilseed plants.

CONCLUSIONS

This work presents the first integrated assay of genome-wide identification of mandelonitrile catabolism-related MDLs and the comparative association of transcriptional level of MDLs with accumulative amount of amygdalin and mandelonitrile in the seeds across different germplasms and developmental periods of P. sibirica to determine MDL2 for mandelonitrile dissociation, and an effective combination of PsMDL2 expression and mutation, oil and mandelonitrile content detection and qRT-PCR assay was performed to unravel a mechanism of PsMDL2 for controlling amygdalin and oil production in P. sibirica seeds. These findings could offer new bioengineering strategy for high oil production with low amygdalin in oil plants.

Review - Plant nutritional status analysis employing the visible and near-infrared spectroscopy spectral sensor

Experiments demonstrated that visible and near-infrared (Vis-NIR) spectroscopy is a highly reliable tool for determining the nutritional status of plants. Although numerous studies on various kinds of plants have been conducted, there are only a few summaries of the research findings regarding the absorbance bands in the visible and near-infrared region and how they relate to the nutritional status of plants. This article will discuss the application of Vis-NIR spectroscopy for monitoring the nutrient conditions of plants, with a particular emphasis on three major components required by plants, namely nitrogen (N), phosphorus (P), and potassium (K), or NPK. Each section discussed different topics, for instance, the essential nutrients needed by plants, the application of Vis-NIR spectroscopy in nutrient status analysis, chemometrics tools, and absorbance bands related to the nutrient status, respectively. Deduction made concluded that factors affecting the plant's structure are contributed by several circumstances like the age of leaves, concentration of pigments, and water content. These factors are intertwined, strongly correlated, and can be observed in the visible and near-infrared regions. While the visible region is commonly utilised for nutritional analysis in plants, the literature review performed in this paper shows that the near-infrared region as well contains valuable information about the plant's nutritional status. A few wavelengths related to the direct estimation of nutrients in this review explained that information on nutrients can be linked with chlorophyll and water absorption bands such that N and P are the components of chlorophyll and protein; on the other hand, K exists in the form of cationic carbohydrates which are sensitive to water region.

The Use of Infrared Spectroscopy for the Quantification of Bioactive Compounds in Food: A Review

Infrared spectroscopy (wavelengths ranging from 750-25,000 nm) offers a rapid means of assessing the chemical composition of a wide range of sample types, both for qualitative and quantitative analyses. Its use in the food industry has increased significantly over the past five decades and it is now an accepted analytical technique for the routine analysis of certain analytes. Furthermore, it is commonly used for routine screening and quality control purposes in numerous industry settings, albeit not typically for the analysis of bioactive compounds. Using the Scopus database, a systematic search of literature of the five years between 2016 and 2020 identified 45 studies using near-infrared and 17 studies using mid-infrared spectroscopy for the quantification of bioactive compounds in food products. The most common bioactive compounds assessed were polyphenols, anthocyanins, carotenoids and ascorbic acid. Numerous factors affect the accuracy of the developed model, including the analyte class and concentration, matrix type, instrument geometry, wavelength selection and spectral processing/pre-processing methods. Additionally, only a few studies were validated on independently sourced samples. Nevertheless, the results demonstrate some promise of infrared spectroscopy for the rapid estimation of a wide range of bioactive compounds in food matrices.

Integrative quantitative and qualitative analysis for the quality evaluation and monitoring of Danshen medicines from different sources using HPLC-DAD and NIR combined with chemometrics

The root and rhizome of Salvia miltiorrhiza (Danshen in short) is a well-known herbal medicine used to treat cardiovascular diseases in the world. In China, the roots and rhizomes of several other Salvia species (Non-Danshen in short) are also used as this medicine in traditional folk medicine by local herbalists. Differences have been reported in these medicines originating from different sources, and their quality variation needs to be clearly investigated for effective clinical application. This study presented a comprehensive quality evaluation and monitoring for Danshen from 27 sampling sites and Non-Danshen from other 5 Salvia species based on a high-performance liquid chromatography-diode array detector (HPLC-DAD) and near-infrared (NIR), with the combination of chemometric models. The results showed that cryptotanshinone, tanshinone IIA, tanshinone I, salvianolic acid B, salvianic acid A sodium, dihydrotanshinone I, and rosmarinic acid in these medicines from different sources exhibited great variations. Referring to the standards in Chinese Pharmacopoeia (CP), European Pharmacopeia (EP), and United States Pharmacopeia (USP), Non-Danshen from S. brachyloma, S. castanea, S. trijuga, S. bowleyana, and S. przewalskii were assessed as unqualified, and Danshen in the Shandong Province had the best quality due to the high qualified rate. Based on random forest (RF) and partial least-squares discriminant analysis (PLS-DA), NIR technique could successfully monitor the quality of these medicines by discriminating the species and regions with the accuracies of 100.00 and 99.60%, respectively. Additionally, modified partial least-squares regression (MPLSR) models were successfully constructed to investigate the feasibility of NIR fingerprints for the prediction of the quality indicators in these medicines. The optimized models obtained the best results for the total of tanshinone IIA, tanshinone I, and cryptotanshinone (TTC), tanshinone IIA, and salvianolic acid B, with the relative prediction deviation (RPD) of 4.08, 3.92, and 2.46, respectively. In summary, this study demonstrated that HPLC-DAD and NIR techniques can complement each other and could be simultaneously applied for evaluating and monitoring the quality of Danshen medicines.

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.

Determination of Quality Parameters in Mangetout (Pisum sativum L. ssp. arvense) by Using Vis/Near-Infrared Reflectance Spectroscopy

Pisum sativum L. ssp. arvense, is colloquially called tirabeque or mangetout because it is eaten whole; its pods are recognized as a delicatessen in cooking due to its crunch on the palate and high sweetness. Furthermore, this legume is an important source of protein and antioxidant compounds. Quality control in this species requires the analysis of a large number of samples using costly and laborious conventional methods. For this reason, a non-chemical and rapid technique as near-infrared reflectance spectroscopy (NIRS) was explored to determine its physicochemical quality (color, firmness, total soluble solids, pH, total polyphenols, ascorbic acid and protein content). Pod samples from different cultivars and grown under different fertigation treatments were added to the NIRS analysis to increase spectral and chemical variability in the calibration set. Modified partial least squares regression was used for obtaining the calibration models of these parameters. The coefficients of determination in the external validation ranged from 0.50 to 0.88. The RPD (standard deviation to standard error of prediction ratio) and RER (standard deviation to range) were variable for quality parameters and showed values that were characteristic of equations suitable for quantitative prediction and screening purposes, except for the total soluble solid calibration model.

Insights on the role of chemometrics and vibrational spectroscopy in fruit metabolite analysis

•

The use of vibrational spectroscopy combined with data analytics is discussed.

•

The measure of bioactive compounds metabolites in fruit samples is presented.

•

Advantages and limitations of these techniques are discussed.

The last three decades have demonstrated the ability of combining data analytics (e.g. big data, machine learning) with modern analytical instrumental techniques such as vibrational spectroscopy (VIBSPEC) (e.g. NIR, Raman, MIR) and sensing technologies (e.g. electronic noses and tongues, colorimetric sensors) to analyse, measure and monitor a wide range of properties and samples. Developments in instrumentation, hardware and software have placed VIBSPEC as a useful tool to quantify several bioactive compounds and metabolites in a wide range of fruit and plant samples. With the incorporation of hand-held and portable instrumentation, these techniques have been valuable for the development of in-field and high throughput applications, opened new frontiers of analysis in fruits and plants. This review will present and discuss some of the current applications on the use of VIBSPEC techniques combined with data analytics on the measurement bioactive compounds and plant metabolites in different fruit samples.

Rapid quantification of dissolved solids and bioactives in dried root vegetable extracts using near infrared spectroscopy

Artificial neural networks (ANN) were developed for prediction of total dissolved solids, polyphenol content and antioxidant capacity of root vegetables (celery, fennel, carrot, yellow carrot, purple carrot and parsley) extracts prepared from the (i) fresh vegetables, (ii) vegetables dried conventionally at 50 °C and 70 °C, and (iii) the lyophilised vegetables. Two types of solvents were used: organic solvents (acetone mixtures and methanol mixtures) and water. Near-infrared (NIR) spectra were recorded for all samples. Principal Component Analysis (PCA) of the pre-treated spectra using Savitzky-Golay smoothing showed specific grouping of samples in two clusters (1st: extracts prepared using methanol mixtures and water as the solvents; 2nd: extracts prepared using acetone mixtures as the solvents) for all four types of extracts. Furthermore, obtained results showed that the developed ANN models can reliably be used for prediction of total dissolved solids, polyphenol content and antioxidant capacity of dried root vegetable extracts in relation to the recorded NIR spectra.

Rapid detection of cellulose and hemicellulose contents of corn stover based on near-infrared spectroscopy combined with chemometrics

The feasibility of near-infrared spectroscopy (NIRS) combined with chemometrics for the rapid detection of the cellulose and hemicellulose contents in corn stover is discussed. Competitive adaptive reweighted sampling (CARS) and genetic simulated annealing algorithm (GSA) were combined (CARS-GSA) to select the characteristic wavelengths of cellulose and hemicellulose and to reduce the dimensionality and multicollinearity of the NIRS data. The whole spectra contained 1845 wavelength variables. After CARS-GSA optimization, the number of characteristic wavelengths of cellulose (hemicellulose) was reduced to 152 (260), accounting for 8.24% (14.09%) of all wavelengths. The coefficients of determination of the regression models for predicting the cellulose and hemicellulose contents were 0.968 and 0.996, the root mean square errors of prediction (RMSEPs) were 0.683 and 0.648, and the residual predictive deviations (RPDs) were 5.213 and 16.499, respectively. The RMSEP of the cellulose and hemicellulose regression models was 0.152 and 0.190 lower for CARS-GSA than for the full-spectrum, and the RPD was increased by 0.949 and 3.47, respectively. The results showed that the CARS-GSA model substantially reduced the number of characteristic wavelengths and significantly improved the predictive ability of the regression model.

Preliminary Study on the Identification of BRAFV600E Mutation in Colorectal Cancer by Near-Infrared Spectroscopy

Introduction

In metastatic colorectal cancer (mCRC), the B-type Raf kinase (BRAF)^V600E mutation is a molecular biomarker of poor prognosis and is of great importance to drug target. Currently, the commonly used methods for detecting BRAF^V600E mutation include immunohistochemistry (IHC) and gene sequencing, but both present certain limitations. Near-infrared (NIR) spectroscopy is a spectroscopy technology that takes advantage of the electromagnetic wavelength between visible light and mid-infrared light.

Methods

IHC was used to detect the expression of BRAF^V600E protein with the BRAF^V600E (VE1) antibody in 42 cases of paraffin-embedded (FFPE) mCRC tissue sections. The NIR-discriminant analysis model (NIRS-DA) was established using 6 cases of wild-type and 6 cases of mutant-type BRAF specimens.

Results

IHC detection results revealed 13 cases of weakly positive (+), 1 case of moderately positive (++), and 28 cases of negative (-) CRC. Compared with the next-generation sequencing (NGS) results, the positive rate was 66.7%. The classification accuracy of calibration (CAC) was 100% compared with the results of NGS, demonstrating that the BRAF^V600E mutant NIRS-DA model, verified by 2 cases of wild-type and 2 cases of mutant-type CRC samples was established. The NIRS-DA model was used to predict gene mutation in the CRC samples, 7 cases were positive (+), and 35 cases were negative (-), and the classification accuracy of prediction (CAP) was 83.3% (35/42).

Discussion

The NIRS-DA model-predicted results were in high agreement with the detection results of NGS, and the difference in IHC is not statistically significant (P>0.05). However, this study is a preliminary discussion on a methodology due to its small sample size.

Investigation on High-Value Bioactive Compounds and Antioxidant Properties of Blackberries and Their Fractions Obtained by Home-Scale Juice Processing

Blackberry pomace represents a valuable but underused byproduct of juice manufacturing. Its further applicability in various food systems is facilitated by detailed knowledge of its own bioactive potential. This study was focused on the investigation of the polyphenolic compound profile, total phenolic and ascorbic acid content, as well as 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity of blackberries (Rubus fruticosus L.) coming from spontaneous flora of two different areas of Romania, Paltinis (Sibiu County) and Zugau (Arad County) and their fractions, juice and pomace, resulting from home-scale processing. To ensure a satisfactory shelf life, the blackberry pomace was subjected to convective drying (60 °C for 12 hours) and the impact of this treatment on the antioxidant properties was evaluated. No significant differences in the investigated characteristics according to the place of origin were recorded. However, a slight increase in the antioxidant properties of fruits and fractions from the Zugau region, characterized by higher temperatures and a lower precipitation regime, was noticed compared with samples derived from the Paltinis area. The drying of blackberry byproducts led to losses of 10–23% in the content of the investigated bioactive compounds and DPPH radical scavenging activity. A significant correlation between DPPH radical scavenging activity and the total phenolic content has been recorded. Our findings are of interest in blackberry selection to enhance the level of bioactive compounds in the targeted products. The obtained results confirm that the blackberry processing byproducts may be regarded as a promising source of high-quality bioactive compounds and a proven radical scavenging capacity, representing a starting point for further analyses. This study responds to a global issue regarding fruit byproduct management in order to ensure the sustainable development of a circular economy.

Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

Mulberry (Morus alba L.) leaves are not only used as the main feed for silkworms (Bombyx mori) but also as an added feed for livestock and poultry. In order to rapidly select high-quality mulberry leaves, a hand-held near-infrared (NIR) spectrometer combined with partial least squares (PLS) regression and wavelength optimization methods were used to establish a predictive model for the quantitative determination of water content in fresh mulberry leaves, as well as crude protein and soluble sugar in dried mulberry leaves. For the water content in fresh mulberry leaves, the R-square of the calibration set (R2 C), R-square of the cross-validation set (R2 CV) and R-square of the prediction set (R2 P) are 0.93, 0.90 and 0.91, respectively, the corresponding root mean square error of calibration set (RMSEC), root mean square error of cross-validation set (RMSECV) and root mean square error of prediction set (RMSEP) are 0.96%, 1.13%, and 1.18%, respectively. The R2 C, R2 CV and R2 P of the crude protein prediction model are 0.91, 0.83 and 0.92, respectively, and the corresponding RMSEC, RMSECV and RMSEP are 0.71%, 0.97% and 0.61%, respectively. The soluble sugar prediction model has R2 C, R2 CV, and R2 P of 0.64, 0.51, and 0.71, respectively, and the corresponding RMSEC, RMSECV, and RMSEP are 2.33%, 2.73%, and 2.36%, respectively. Therefore, the use of handheld NIR spectrometers combined with wavelength optimization can fastly detect the water content in fresh mulberry leaves and crude protein in dried mulberry leaves. However, it is a slightly lower predictive performance for soluble sugar in mulberry leaves.

Determination of the Total Phenolic Content in Wine Samples Using Potentiometric Method Based on Permanganate Ion as an Indicator

A rapid and accurate determination method for total phenolic content is of great importance for controlling the quality of wine samples. A promising potentiometric detection approach, based on permanganate ion fluxes across ion-selective electrode membranes, is fabricated for measuring the total phenolic content of wine. The results show that the presence of phenols, such as gallic acid, leads to a potential increase for the potentiometric sensor. Additionally, the present sensor exhibits a linear potential response with the concentration range from 0.05 to 3.0 g/L with a detection limit of 6.6 mg/L calculated using gallic acid. These sensors also exhibit a fast response time, an acceptable reproducibility and long-term stability. These results indicate that the proposed potentiometric sensor can be a promising and reliable tool for the rapid determination of total phenolic content in wine samples.

Detection of the BRAF V600E Mutation in Colorectal Cancer by NIR Spectroscopy in Conjunction with Counter Propagation Artificial Neural Network

This paper proposes a sensitive, sample preparation-free, rapid, and low-cost method for the detection of the B-rapidly accelerated fibrosarcoma (BRAF) gene mutation involving a substitution of valine to glutamic acid at codon 600 (V600E) in colorectal cancer (CRC) by near-infrared (NIR) spectroscopy in conjunction with counter propagation artificial neural network (CP-ANN). The NIR spectral data from 104 paraffin-embedded CRC tissue samples consisting of an equal number of the BRAF V600E mutant and wild-type ones calibrated and validated the CP-ANN model. As a result, the CP-ANN model had the classification accuracy of calibration (CAC) 98.0%, cross-validation (CACV) 95.0% and validation (CAV) 94.4%. When used to detect the BRAF V600E mutation in CRC, the model showed a diagnostic sensitivity of 100.0%, a diagnostic specificity of 87.5%, and a diagnostic accuracy of 93.8%. Moreover, this method was proven to distinguish the BRAF V600E mutant from the wild type based on intrinsic differences by using a total of 312 CRC tissue samples paraffin-embedded, deparaffinized, and stained. The novel method can be used for the auxiliary diagnosis of the BRAF V600E mutation in CRC. This work can expand the application of NIR spectroscopy in the auxiliary diagnosis of gene mutation in human cancer.