Quantitative assessment of phytochemicals in chickpea beverages using NIR spectroscopy

The prospects of near-infrared (NIR) spectroscopy combined with effective variable selection algorithms for quantifying phytochemical compounds in chickpea beverages were investigated in this study. As reference measurement analysis, the phytochemicals were extracted and identified via high-performance liquid chromatography. Multivariate algorithms were then applied, analyzed, and evaluated using correlation coefficients of validation set (Rp), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). Accordingly, the competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model achieved superior performance for biochanin A (Rp = 0.933, RPD = 3.63), chlorogenic acid (Rp = 0.928, RPD = 3.52), p-coumaric acid (Rp = 0.900, RPD = 2.37), and stigmasterol (Rp = 0.932, RPD = 3.15), respectively. Hence, this study demonstrated that NIR spectroscopy paired with CARS-PLS could be used for nondestructive quantitative prediction of phytochemicals in chickpea beverages during manufacture and storage.

Characterization, antioxidant activity and potential application fractionalized Szechuan pepper on fresh beef meat as natural preservative

The pericarp of Szechuan pepper is rich in phenols and alkylamides, making it a potential source of antioxidant compounds. Despite being recognized as the primary antioxidants in Szechuan pepper, there is still limited knowledge about their application in real food systems. This study aims to identify, separate, and apply polyphenol and alkylamide fractions derived from Szechuan extracts to beef meat. Using HPLC-MS2, we identified 5 phenols and 11 alkylamides in Szechuan extracts. The quality of the minced meat was evaluated based on color, thiobarbituric acid reactive substances (TBARS), conjugated dienes, carbonyl content, Sulfhydryl content, microbiological content, and total volatile basic nitrogen content (TVB-N). Compared to the polyphenol fraction (1.25 mg/mL), alkylamide fraction (25 mg/mL), and control samples, beef samples incorporated with the polyphenol fraction (6.25 mg/mL) significantly reduced carbonyl content, TBARS, and TVB-N values at the end of storage. Furthermore, they exhibited a significant slowdown in microbial development, improved meat color stability, and preserved pH. Therefore, the use of Szechuan pepper fractions as natural preservatives in meat and meat products is an important area of research and has the potential to enhance the safety and quality of meat products.

Recent advances in rare earth ion-doped upconversion nanomaterials: From design to their applications in food safety analysis

The misuse of chemicals in agricultural systems and food production leads to an increase in contaminants in food, which ultimately has adverse effects on human health. This situation has prompted a demand for sophisticated detection technologies with rapid and sensitive features, as concerns over food safety and quality have grown around the globe. The rare earth ion-doped upconversion nanoparticle (UCNP)-based sensor has emerged as an innovative and promising approach for detecting and analyzing food contaminants due to its superior photophysical properties, including low autofluorescence background, deep penetration of light, low toxicity, and minimal photodamage to the biological samples. The aim of this review was to discuss an outline of the applications of UCNPs to detect contaminants in food matrices, with particular attention on the determination of heavy metals, pesticides, pathogenic bacteria, mycotoxins, and antibiotics. The review briefly discusses the mechanism of upconversion (UC) luminescence, the synthesis, modification, functionality of UCNPs, as well as the detection principles for the design of UC biosensors. Furthermore, because current UCNP research on food safety detection is still at an early stage, this review identifies several bottlenecks that must be overcome in UCNPs and discusses the future prospects for its application in the field of food analysis.

Flexible SERS sensor using AuNTs-assembled PDMS film coupled chemometric algorithms for rapid detection of chloramphenicol in food

The misuse of chloramphenicol (CAP) has led to the development of drug-resistant strains that pose significant threats to public health. Here, we propose a universal flexible surface-enhanced Raman spectroscopy (SERS) sensor utilizing gold nanotriangles (AuNTs) and polydimethylsiloxane (PDMS) film for rapid detection of CAP in food samples. Initially, AuNTs@PDMS with unique optical and plasmonic properties were used to collect spectra of CAP. Afterward, four chemometric algorithms were executed and compared. Accordingly, random frog-partial least squares (RF-PLS) exhibited optimum results with correlation coefficient of prediction (Rp = 0.9802) and the lowest root-mean-square error of prediction (RMSEP = 0.348 µg/mL). Furthermore, the sensor's efficacy to detect CAP in milk samples was confirmed, and the findings were compatible with the conventional HPLC approach (P > 0.05). Therefore, the proposed flexible SERS sensor could effectively be used to monitor milk quality and safety.

Au-Ag OHCs-based SERS sensor coupled with deep learning CNN algorithm to quantify thiram and pymetrozine in tea

Pesticide residue detection in food has become increasingly important. Herein, surface-enhanced Raman scattering (SERS) coupled with an intelligent algorithm was developed for the rapid and sensitive detection of pesticide residues in tea. By employing octahedral Cu₂O templates, Au-Ag octahedral hollow cages (Au-Ag OHCs) were developed, which improved the surface plasma effect via rough edges and hollow inner structure, amplifying the Raman signals of pesticide molecules. Afterward, convolutional neural network (CNN), partial least squares (PLS), and extreme learning machine (ELM) algorithms were applied for the quantitative prediction of thiram and pymetrozine. CNN algorithms performed optimally for thiram and pymetrozine, with correlation values of 0.995 and 0.977 and detection limits (LOD) of 0.286 and 29 ppb, respectively. Accordingly, no significant difference (P greater than 0.05) was observed between the developed approach and HPLC in detecting tea samples. Hence, the proposed Au-Ag OHCs-based SERS technique could be utilized for quantifying thiram and pymetrozine in tea.

Fusion-based strategy of CSA and mobile NIR for the quantification of free fatty acid in wheat varieties coupled with chemometrics

The use of sensor fusion, a novel method of combining artificial senses, has become increasingly popular in the assessment of food quality. This study employed a combination of the colorimetric sensor array (CSA) and mobile near-infrared (NIR) spectroscopy to predict free fatty acids in wheat flour. In conjunction with a partial least squares model, Low- and mid-level fusion strategies were used for quantification. Accordingly, performance of the built model was evaluated based on higher correlation coefficients between calibration and prediction (R_C and R_P), lower root mean square error of prediction (RMSEP), and a higher residual predictive deviation (RPD). The mid-level fusion coupled PLS model produced superior data fusion findings, with R_C = 0.8793, RMSECV = 7.91 mg/100 g, R_P = 0.8747, RMSEP = 6.99 mg/100 g, and RPD = 2.27. The findings of the study suggest that the NIR-CSA fusion approach could be effectively applied to the prediction of free fatty acids in wheat flour.

Ultrasensitive fluorescence sensor for Hg2+ in food based on three-dimensional upconversion nanoclusters and aptamer-modulated thymine-Hg2+-thymine strategy

Mercury (Hg²⁺) is a potentially toxic heavy metal ion found to be drastically deleterious to humans. Herein, an ultrasensitive fluorescence sensor was developed using three-dimensional upconversion nanoclusters (EBSUCNPs) and aptamer-modulated thymine-Hg²⁺-thymine strategy. The EBSUCNPs were used as the energy donors, the PDANPs served as the acceptors, and the aptamer was applied as an identification tag for Hg²⁺. Due to the energy transfer effect, the fluorescence of EBSUCNPs can be effectively quenched by Polydopamine nanoparticles (PDANPs). In the existence of Hg²⁺, T (thymine)-rich aptamers between EBSUCNPs and PDANPs were hybridized with Hg²⁺ to yield thymine-Hg²⁺-thymine and folded back to hairpin structure, causing PDANPs to detach from the EBSUCNPS and the recovery of fluorescence. Under optimum conditions, the linear sensing range of Hg²⁺ was 0.5-20 µg/L, and the detection limit was 0.28 µg/L. Furthermore, it exhibited excellent selectivity and anti-interference, which made it an ideal method for identifying Hg²⁺ in spiked samples.

Stimuli-responsive SERS biosensor for ultrasensitive tetracycline sensing using EDTA-driven PEI@CaCO3 microcapsule and CS@FeMMs

In this work, a stimuli-responsive SERS biosensor was fabricated for tetracycline (TTC) by "signal-on" strategy using (EDTA)-driven polyethyleneimine grafted calcium carbonate (PEI@CaCO₃) microcapsule and chitosan-Fe magnetic microbeads (CS@FeMMs). Initially, aptamer conjugated magnetic-bead CS@FeMMs@Apt with superparamagnetism and excellent biocompatibility was employed as capture probe, which facilitated the rapid and easy magnetic separation. Subsequently, the PEI cross-linked layer and aptamer network layer were constructed onto the outer layer of CaCO₃@4-ATP microcapsule to form sensing probes (PEI@CaCO₃@4-ATP@Apt) via the layer-by-layer assembly method. In the presence of TTC, a sandwich SERS-assay was exploited by aptamer recognition induced target-bridged strategy. When the solution of EDTA was added, the core layer of CaCO₃ would be dissolved quickly, destroying the microcapsule to release 4-ATP. The released 4-ATP could be quantitatively monitored by dripping the supernatant onto the AuNTs@PDMS SERS platform, resulting in a strong Raman "signal-on". Under the optimal conditions, a good linear relationship was established with a correlation coefficient (R²) of 0.9938 and a LOD of 0.03 ng/mL. Additionally, the application capacity of the biosensor to detect TTC was also affirmed in food matrixes, and the results were consistent with the standard ELISA method (P > 0.05). Hence, this SERS biosensor affords extensive application prospects for TTC detection with multiple merits such as high sensitivity, environment friendliness, and high stability.

Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms

Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.

The anti-oxidative potential of ginger extract and its constituent on meat protein isolate under induced Fenton oxidation

Ginger extract has been reported to possess antioxidant properties. However, components isolated from ginger have been rarely reported to inhibit oxidation. Herein, the antioxidant properties of ginger and purified components derived from it (6-gingerol, zingerone, rutin, quercetin, and kaempferol) were confirmed by using HPLC and were further used to investigate its effect on lamb meat. Myofibrillar proteins isolated (MPI) from lamb meat were incubated with ginger and its constituents under induced Fenton oxidation (1.0 mmol/L FeCl3, 0.1 mmol/L Asc, and 20 mmol/L H2O2) for 1, 3,5, and 7 h. Incubating meat protein isolate in the absence of ginger extract or its components resulted in a substantial drop in sulfhydryl groups, an increase in protein carbonyl content, and a corresponding increase in TBARS content. However, ginger extract and its constituents demonstrated antioxidant properties, which might be attributed to their hydroxyl groups and suitable solubilizing side chains. Overall, ginger extract exhibited the highest antioxidant capabilities of all treated samples, suggesting that ginger extracts may be used as a natural antioxidant in meat and lipid/protein-containing processed products. SIGNIFICANCE OF THE STUDY: Ginger extract is also frequently used as a herbal medicine due to its anti-inflammatory, anti-cancer, and antibacterial qualities. Nonvolatile pungent chemicals found in ginger, such as gingerol, shogaols, paradols, and zingerone, as well as kaempferol, rutin, and other phenolic compounds, have been confirmed in ginger extract and have been shown to have antioxidant action driven by free radical elimination. Despite these findings, ginger extract and its pure constituent components have seldom been shown to have the ability to slow protein and lipid oxidation in meat and meat-related products. The effect of ginger extracts on the oxidative stability of myofibriller protein isolate has never been investigated. Exploiting the phenolic content of ginger extract may result in a discovery that would have a huge influence on both the ginger and meat industries as well as other food processing sectors. The first aim of our study was to confirm the presence of six selected phenolic compounds (rutin, kaempferol, 6-gingerol, zingerone, naringenin, and quercetin) in ginger as reported by literature, and the second objective was to determine the efficacy of ginger extracts and its purified constituents on myofibrillar protein isolate treated under induced Fenton oxidation.

Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration

Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (R_p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (R_p = 0.937, RPD = 3.38) and CP (R_p = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.

Fraud detection in crude palm oil using SERS combined with chemometrics

Edible crude palm oil (CPO) is a vital oil utilized in various industries, including food, pharmaceuticals, and domestic cooking. Unfortunately, reports of CPO adulteration with harmful Sudan dyes have surfaced over the years. Surface-enhanced Raman spectroscopy (SERS) and chemometrics were employed to detect Sudan dyes adulteration in CPO within 900 - 1800 cm^{- 1} Raman peak. The concentration of Sudan dyes detected in CPO samples ranged between 0.005 and 4 ppm. The principal component analysis (PCA) model detected Sudan II and Sudan IV in CPO with 99.88 and 99.90% accuracy. Linear discriminant analysis (LDA) and K-Nearest Neighbors (KNN) also recorded high detection rates of Sudan II and IV dyes in CPO. Sudan II and IV dyes could be detected at 0.0028 ppm and 0.0019 ppm by this sensor. The performance of the Au@Ag SERS sensor was comparable to that of HPLC. This study proved SERS and chemometrics can be used to authenticate edible CPO.

Tunable multiplexed fluorescence biosensing platform for simultaneous and selective detection of paraquat and carbendazim pesticides

The monitoring of multiple pesticides commonly used in food is a prerequisite for public health safety. Herein, a multiplexed biosensor based on fluorescence resonance energy transfer (FRET) from multicolor upconversion nanoparticles (UCNPs)to single black phosphorus nanosheets (BPNSs) was successfully developed for simultaneous and selective detection of paraquat and carbendazim pesticides. Due to the strong π-π stacking interactions, aptamers functionalized UCNPs may adsorb on the BPNSs surface, allowing strong upconversion fluorescence quenching. In the presence of paraquat and carbendazim, the aptamers preferentially integrated with their corresponding targets and altered the aptamer's conformation, restoring the fluorescence. An excellent linear correlation was observed from 1.0 to 1.0 × 10⁵ ng/mL, with a limit of detection of 0.18 ng/mL for paraquat and 0.45 ng/mL for carbendazim. The developed aptasensor was further validated by commercial enzyme-linked immunoassays without significant differences in practical detection. Additionally, this work offers new insights into monitoring multiple targets simultaneously.

An Up-conversion signal probe-MnO2 nanosheet sensor for rapid and sensitive detection of tetracycline in food

The irrational use of tetracycline (TC) poses a serious threat to human health, which calls for the development of efficient and reliable detection methods. Herein, an ideal sensor based on luminescence resonance energy transfer (LRET) between aptamer modified up-conversion nanoparticles as signal probes (donors) and manganese dioxide (MnO₂) nanosheets (acceptors) was developed for TC detection in food samples. As a result of van der Waals forces between the nucleobases of the aptamer and the basal plane of MnO₂ nanosheets, the distance of the donors and acceptors was shortened. The emission spectrum of the signal probes and the absorption spectrum of MnO₂ nanosheets overlapped, resulting in LRET, and quenching of up-conversion luminescence. The TC-specific aptamer could fold into a complex conformational structure to provide recognition sites for TC. In the presence of TC, the aptamer was found to preferentially combine with TC due to the stacking of planar moieties, hydrogen bonding interactions and molecular shape complementarity, causing the separation of signal probes and nanosheets, and luminescence recovery. Consequently, a low detection limit of 0.0085 ng/mL was achieved with a wide detection range of 0.01-100 ng/mL. Moreover, the ability of the sensor to detect TC was confirmed in actual food samples and compared with the traditional ELISA with satisfactory results (p > 0.05).

Identification of characteristic volatiles and metabolomic pathway during pork storage using HS-SPME-GC/MS coupled with multivariate analysis

Previous researches have been conducted evaluating the volatile compounds of pork. However, data regarding the changes in volatiles and metabolic pathways during pork storage were inadequately investigated. Herein, a headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) coupled multivariate analysis was proposed for characterizing the profiles of volatile compounds and metabolic pathways during pork storage. A total of 37 metabolites, including aldehydes, ketones, alcohols etc. were successfully identified. Multivariate statistical analysis revealed a substantial variation in metabolite phenotype among samples over the pork storage period, with 12 characteristic metabolites and 5 potential characteristic metabolites screened as biomarkers. Moreover, three metabolomic pathways analysis and transformation between each other (thermal reactions, lipid metabolism and amino acid metabolism) reveals the underlying mechanisms of metabolites change of pork. Therefore, the present study may provide insight into future understanding of the variation in the pork metabolite profiles.

Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration

Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models' performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin.

Application of NIR spectroscopy for rapid quantification of acid and peroxide in crude peanut oil coupled multivariate analysis

Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil.