Measurement of total anthocyanins content in flowering tea using near infrared spectroscopy combined with ant colony optimization models

A Review of Quantitative and Topical Analysis of Anthocyanins in Food

Anthocyanins, a subclass of flavonoids known for their vibrant colors and health-promoting properties, are pivotal in the nutritional science and food industry. This review article delves into the analytical methodologies for anthocyanin detection and quantification in food matrices, comparing quantitative and topical techniques. Quantitative methods, including High-performance Liquid Chromatography (HPLC) and Mass Spectrometry (MS), offer precise quantification and profiling of individual anthocyanins but require sample destruction, limiting their use in continuous quality control. Topical approaches, such as Near-infrared Spectroscopy (NIR) and hyperspectral imaging, provide rapid, in situ analysis without compromising sample integrity, ideal for on-site food quality assessment. The review highlights the advancements in chromatographic techniques, particularly Ultra-high-performance Liquid Chromatography (UHPLC) coupled with modern detectors, enhancing resolution and speed in anthocyanin analysis. It also emphasizes the growing importance of topical techniques in the food industry for their efficiency and minimal sample preparation. By examining the strengths and limitations of both analytical realms, this article aims to shed light on current challenges and prospective advancements, providing insights into future research directions for improving anthocyanin analysis in foods.

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.

Distribution of and Temporal Variation in Volatiles in Tea (Camellia sinensis) Flowers during the Opening Stages

Tea (Camellia sinensis) flowers emit a large amount of volatiles that attract pollinators. However, few studies have characterized temporal and spatial variation in tea floral volatiles. To investigate the distribution of volatiles within tea flowers and their variation among opening stages, volatile components from different parts of tea flowers and different opening stages were collected by headspace solid-phase microextraction and analyzed by gas chromatography-mass spectrometry. A total of 51 volatile compounds of eight chemical classes were identified in the tea flowers. Volatile compounds were most abundant in tea flowers of the Shuchazao cultivar. Acetophenone, 1-phenylethanol, 2-phenylethanol, and benzyl alcohol were the most abundant volatiles. Terpenes were common in the sepals, and benzoids were common in the stamens. The fatty acid derivatives were mainly distributed in the pistils and receptacles and were less abundant in the petals, sepals, and stamens. During the opening phase of tea flowers, the volatile content increased 12-fold, which mainly stemmed from the increase in benzoids. These results enhance our understanding of the formation of volatiles in tea flowers.

Effects of slit dual-frequency ultrasound-assisted pulping on the structure, functional properties and antioxidant activity of Lycium barbarum proteins and in situ real-time monitoring process

To improve the protein dissolution rate and the quality of fresh Lycium barbarum pulp (LBP), we optimized the slit dual-frequency ultrasound-assisted pulping process, explored the dissolution kinetics of Lycium barbarum protein (LBPr), and established a near-infrared spectroscopy in situ real-time monitoring model for LBPr dissolution through spectral information analysis and chemometric methods. The results showed that under optimal conditions (dual-frequency 28-33 kHz, 300 W, 31 min, 40 °C, interval ratio 5:2 s/s), ultrasonic treatment not only significantly increased LBPr dissolution rate (increased by 71.48 %, p < 0.05), improved other nutrient contents and color, but also reduced the protein particle size, changed the amino acid composition ratio and protein structure, and increased the surface hydrophobicity, zeta potential, and free sulfhydryl content of protein, as well as the antioxidant activity of LBPr. In addition, ultrasonication significantly improved the functional properties of the protein, including thermal stability, foaming, emulsification and oil absorption capacity. Furthermore, the real-time monitoring model of the dissolution process was able to quantitatively predict the dissolution rate of LBPr with good calibration and prediction performance (Rc = 0.9835, RMSECV = 2.174, Rp = 0.9841, RMSEP = 1.206). These findings indicated that dual-frequency ultrasound has great potential to improve the quality of LBP and may provide a theoretical basis for the establishment of an intelligent control system in the industrialized production of LBP and the functional development of LBPr.

Camellia japonica Flowers as a Source of Nutritional and Bioactive Compounds

In recent decades, plants have strengthened their relevance as sources of molecules potentially beneficial for health. This underpinning effect also arises from the extensive research that has been conducted on plants that are typically undervalued, besides being scarcely used. This is the case with Camellia japonica in Galicia (NW Spain), where, despite its abundance, it is exclusively used for ornamental purposes and has been studied only for its proximate composition. Thus, the present study was conducted on several additional parameters in the flowers of eight C. japonica varieties. Our results show that camellia has a high nutritional value, with carbohydrates as the most abundant macronutrients followed by a moderate protein content (4.4-6.3 g/100 g dry weight) and high levels of polyunsaturated fatty acids (especially ω-3 fatty acids, which represent 12.9-22.7% of the total fatty acids), raising its potential for use for nutritional purposes. According to the thermochemical characterization and elemental composition of camellia, the raw material has poor mineralization and low nitrogen content, but high percentages of volatile matter and high carbon-fixation rates, making it a promising alternative for biofuel production. Furthermore, preliminary analysis reveals a high concentration of different bioactive compounds. As a result of these findings, camellias can be used as food or functional ingredients to improve the nutritional quality of food formulations.

Prediction of Anthocyanidins Content in Purple Chinese Cabbage Based on Visible/Near Infrared Spectroscopy

Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.

In-situ and real-time monitoring of two-stage enzymatic preparation of ACE inhibitory peptides from Cordyceps militaris medium residues by ultrasonic-assisted pretreatment

A protocol for the preparation of angiotensin-Ⅰ-converting enzyme (ACE) inhibitory peptides from Cordyceps militaris medium residues (CMMR) was established by two-stage enzymatic hydrolysis (amylase and protease). In a combination (mono-, dual-, and tri-frequency) of five different frequencies (20, 28, 35, 40 and 50 kHz), ultrasound-assisted pretreatment increased ACE inhibition rate in hydrolysate by 63.30 % under the mode of 20/28 kHz. Afterwards, near-infrared (NIR) spectrometer combined with spectral preprocessing methods and multivariate analysis like partial least square (PLS), synergy interval-PLS (Si-PLS), random frog-PLS (RF-PLS) and competitive adaptive reweighted sampling (CARS-PLS) was used to monitor the ACE inhibitory activity. The performance of models was evaluated by the correlation coefficient (R_p) and root mean square error (RMSEP). CARS-PLS models achieved optimal results for both amylase and protease hydrolysis with R_p = 0.9693, RMSEP = 0.01 for the former and R_p = 0.9454, RMSEP = 0.03 for the latter. NIR spectrometer combined with CARS-PLS models may be employed for in-situ and real-time monitoring of the preparation of ACE inhibitory peptides under ultrasonic-assisted pretreatment.

Transcriptomic analysis reveals biosynthesis genes and transcription factors related to leaf anthocyanin biosynthesis in Aglaonema commutatum

BACKGROUND

Aglaonema commutatum 'Red Valentine', as a foliage ornamental plant, is widely used for interior and exterior decoration because of its easy cultivation and management. However, reduced proportion of red foliage during large-scale production of A. commutatum seedlings is a frequent occurrence, which has considerable implications on the plant's ornamental and market value. However, the molecular mechanisms underlying this phenomenon remain unclear.

RESULTS

To explore the molecular basis of the variation in leaf color of A. commutatum Red Valentine, we performed transcriptome sequencing with the Illumina platform using two different varieties of A. commutatum, namely Red Valentine and a green mutant, at three different stages of leaf development. We annotated 63,621 unigenes and 14,186 differentially expressed genes by pairwise comparison. Furthermore, we identified 26 anthocyanin biosynthesis structural genes. The transcript per million (TPM) values were significantly higher for Red Valentine than for the green mutant in all three developmental stages, consistent with the high anthocyanin content of Red Valentine leaves. We detected positive transcription factors that may be involved in the regulation of anthocyanin biosynthesis using BLAST and through correlation analysis. Downregulation of these transcription factors may downregulate the expression of anthocyanin genes. We obtained full-length cDNA of the anthocyanin biosynthesis and regulatory genes and constructed phylogenetic trees to ensure accuracy of the analysis.

CONCLUSIONS

Our study provides insights into the molecular mechanisms underlying leaf variation in A. commutatum Red Valentine and may be used to facilitate the breeding of ornamental cultivars with high anthocyanin levels.

Toward in-process technology-aided automation for enhanced microbial food safety and quality assurance in milk and beverages processing

Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.

A Narrative Review of Recent Advances in Rapid Assessment of Anthocyanins in Agricultural and Food Products

Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.

Application of bio-inspired optimization algorithms in food processing

Bio-inspired optimization techniques (BOT) are part of intelligent computing techniques. There are several BOTs available and many new BOTs are evolving in this era of industrial revolution 4.0. Genetic algorithm, particle swarm optimization, artificial bee colony, and grey wolf optimization are the techniques explored by researchers in the field of food processing technology. Although, there are other potential methods that may efficiently solve the optimum related problem in food industries. In this review, the mathematical background of the techniques, their application and the potential microbial-based optimization methods with higher precision has been surveyed for a complete and comprehensive understanding of BOTs along with their mechanism of functioning. These techniques can simulate the process efficiently and able to find the near-to-optimal value expeditiously.

Convenient use of near-infrared spectroscopy to indirectly predict the antioxidant activitiy of edible rose (Rose chinensis Jacq "Crimsin Glory" H.T.) petals during infrared drying

Infrared drying (IRD) was used for the dehydration process of rose petals for the purpose of improving drying efficiency as well as retaining product quality. A methodology to predict the antioxidant capacities of rose petals which include DPPH, ABTS radical scavenging capacities and ferric-ion reducing antioxidant power (FRAP) values during infrared drying (IRD) was established in this study. Partial least squares regression (PLSR) and back propagation-artificial neural network (BP-ANN) modelling were used to establish the relationships between the near infrared (NIR) spectrum and the antioxidant capacities. Results of model fitting showed that BP-ANN model displayed higher prediction accuracy than PLSR model for determining the DPPH, ABTS radical scavenging capacities and FRAP of rose petals during IRD based on NIR spectral data. The results obtained indicate that NIR spectroscopic parameters combined with multivariate calibration could be used reliably to predict the antioxidant capacities of IR-dried rose petals via appropriate mathematical models.

Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds

Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895–2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with R2 over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (R2) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained R2 and SEP values of the seed model were comparable to the result of powder of 0.92–0.95 and 0.09–0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content.

Analysis of Conformational, Structural, Magnetic, and Electronic Properties Related to Antioxidant Activity: Revisiting Flavan, Anthocyanidin, Flavanone, Flavonol, Isoflavone, Flavone, and Flavan-3-ol

Understanding the antioxidant activity of flavonoids is important to investigate their biological activities as well as to design novel molecules with low toxicity and high activity. Aromaticity is a chemical property found in cyclic structures that plays an important role in their stability and reactivity, and its investigation can help us to understand the antioxidant activity of some heterocyclic compounds. In the present study, we applied the density functional theory (DFT) to investigate the properties of seven flavonoid structures with well-reported antioxidant activity: flavan, anthocyanidin, flavanone, flavonol, isoflavone, flavone, and flavan-3-ol. Conformational, structural, magnetic, and electronic analyses were performed using nuclear magnetic resonance, ionization potentials, electron affinity, bond dissociation energy, proton affinity, frontier molecular orbitals (highest occupied molecular orbital (HOMO)/lowest unoccupied molecular orbital (LUMO)), and aromaticity through nucleus-independent chemical shifts to analyze these seven flavonoid structures. We revised the influence of hydroxyl groups on the properties of flavonoids and also investigated the influence of the aromaticity of these seven flavonoids on the antioxidant activity.

Luteolin-induced activation of the phenylpropanoid metabolic pathway contributes to quality maintenance and disease resistance of sweet cherry

Redox imbalance and fungal infection are major causes for quality deterioration and postharvest decay of fruit. Therefore, it is crucial to activate intrinsic antioxidative capacity and disease responses for fruit quality maintenance. Although plant-derived flavonoids have been reported for health-promoting benefits, their roles in the maintenance of fruit quality remains largely unexplored. Here, we exogenously applied luteolin, a flavonoid substance, and further examined its efficacy in maintaining fruit quality and inhibiting fungal diseases in sweet cherry. The results showed that 100 or 200 mg/L luteolin maintained better organoleptic quality and decreased disease incidence during storage. Biochemical assays revealed that luteolin activated the phenylpropanoid metabolic pathway and improved antioxidative capacity, thereby elevating total anthocyanin and flavonoid contents. Notably, luteolin inhibited mycelial growth of fungal pathogens and reduced patulin yield by Penicillium expansum. Collectively, these results suggest that luteolin is a promising alternative for maintaining better fruit quality and ameliorating disease resistance.

Genomic Survey, Transcriptome, and Metabolome Analysis of Apocynum venetum and Apocynum hendersonii to Reveal Major Flavonoid Biosynthesis Pathways

Apocynum plants, especially A. venetum and A. hendersonii, are rich in flavonoids. In the present study, a whole genome survey of the two species was initially carried out to optimize the flavonoid biosynthesis-correlated gene mining. Then, the metabolome and transcriptome analyses were combined to elucidate the flavonoid biosynthesis pathways. Both species have small genome sizes of 232.80 Mb (A. venetum) and 233.74 Mb (A. hendersonii) and showed similar metabolite profiles with flavonols being the main differentiated flavonoids between the two specie. Positive correlation of gene expression levels (flavonone-3 hydroxylase, anthocyanidin reductase, and flavonoid 3-O-glucosyltransferase) and total flavonoid content were observed. The contents of quercitrin, hyperoside, and total anthocyanin in A. venetum were found to be much higher than in A. hendersonii, and such was thought to be the reason for the morphological difference in color of A. venetum and A. hendersonii. This study provides valuable genomic and metabolome information for understanding of A. venetum and A. hendersonii, and lays a foundation for elucidating Apocynum genus plant flavonoid biosynthesis.

Comparative study on the antioxidant activities of ten common flower teas from China

Flower teas are widely consumed across the world because of their beneficial health effects. The antioxidant activities of methanol extracts from ten common flower teas in China were evaluated using four antioxidant assays. The total phenolic (TPCs) and total flavonoid contents (TFCs) were also investigated. Most of these flower teas exhibited potent antioxidant effects, of which rosae rugosae flos exerted the strongest antioxidant effects in four assays. Rosae rugosae flos also exerted the highest TPC, while fragrans showed the highest TFC. Correction analysis indicated that phenolics play a key role in the antioxidant effect of flower teas, while flavonoids were poorly correlated with antioxidant activity. The results supported the consumption of flower teas as functional foods and their application as sources of natural antioxidants in the food industry.

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350-2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745-910 nm and 1,911-2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSE t and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSE t was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

Survey of Anthocyanin Composition and Concentration in Diverse Maize Germplasms

Increasing consumer demand for natural ingredients in foods and beverages justifies investigations into more economic sources of natural colorants. In this study, 398 genetically diverse pigmented accessions of maize were analyzed using HPLC to characterize the diversity of anthocyanin composition and concentration in maize germplasm. One hundred and sixty-seven accessions were identified that could produce anthocyanins in the kernel pericarp or aleurone and were classified into compositional categories. Anthocyanin content was highest in pericarp-pigmented accessions with flavanol-anthocyanin condensed forms, similar to the Andean Maı́z Morado landraces. A selected subset of accessions exhibited high broad-sense heritability estimates for anthocyanin production, indicating this trait can be manipulated through breeding. This study represents the most comprehensive screening of pigmented maize lines to date and will provide information to plant breeders looking to develop anthocyanin-rich maize hybrids as an economic source of natural colorants in foods and beverages.

Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of spectra of the jujube (Zizyphus jujuba Mill.) fruit samples from four geographical regions. Prediction models were developed for the quantitative prediction of the contents of jujube fruit, i.e., total sugar, total acid, total phenolic content, and total antioxidant activity. Four pattern recognition methods, principal component analysis (PCA), linear discriminant analysis (LDA), least squares-support vector machines (LS-SVM), and back propagation-artificial neural networks (BP-ANN), were used for the geographical origin classification. Furthermore, three multivariate calibration models based on the standard normal variate (SNV) pretreated NIR spectroscopy, partial least squares (PLS), BP-ANN, and LS-SVM were constructed for quantitative analysis of the four analytes described above. PCA provided a useful qualitative plot of the four types of NIR spectra from the fruit. The LS-SVM model produced best quantitative prediction results. Thus, NIR spectroscopy in conjunction with chemometrics, is a very useful and rapid technique for the discrimination of jujube fruit.

Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms

Hyperspectral imaging (HSI) system has been used to assess the chicken quality in this work. Principle component analysis (PCA) and Ant Colony Optimization (ACO) were comparatively used for data dimension reduction. First, we selected 5 dominant wavelength images from chicken hypercube using PCA and ACO. Then, 6 textural variables based on statistical moments were extracted from each dominant wavelength image, thus totaling to 30 variables. Next, we selected the classic back propagation artificial neural network (BPANN) algorithm for modeling. Experimental results showed the performance of ACO-BPANN model is superior to that of PCA-BPANN model, and the optimum ACO-BPANN model was achieved with RMSEP=6.3834 mg/100g and R=0.7542 in the prediction set. Our work implies that HSI integrating spectral and spatial information has a high potential in quantifying TVB-N content of chicken in rapid and non-destructive manner, and ACO has superiority in dimension reduction of hypercube.

In vitro and in vivo biological activities of anthocyanins from Nitraria tangutorun Bobr. fruits

Anthocyanins are the main compounds in Nitraria tangutorun Bobr. The enrichment and purification of anthocyanins on macroporous resins were investigated. Regarding anthocyanin purification, static adsorption and desorption were studied. The optimal experimental conditions were the following: resin type: X-5; static adsorption time: 6h; desorption solution: ethanol-water-HCl (80:19:1, V/V/V; pH 1); desorption time: 40min. Furthermore, the in vitro and in vivo biological activities of the anthocyanins were evaluated. The anthocyanins showed ideal scavenging effects on free radicals in vitro, especially on 1,1-diphenyl-2-picrylhydrazyl (DPPH) and hydroxyl free radical (OH). In the animal experiment, blood lipid metabolism of hyperlipidemia rats was regulated by anthocyanin contents. The superoxide dismutase (SOD) activity and the total antioxidant capacity (TAC) of hyperlipidemia rats were also improved by anthocyanins. These results showed that anthocyanins from N. tangutorun Bobr. fruits had potential biological activities in vivo as well as in vitro.

Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm

The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc=0.95 and RMSEC=1.12 in the calibration set, Rp=0.95 and RMSEP=1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine.

Non-destructively sensing pork quality using near infrared multispectral imaging technique

Near infrared multispectral imaging system based on three wavebands—1280 nm, 1440 nm and 1660 nm—was developed for the non-destructive sensing of the tenderness and water holding capacity of pork.