Application of multiplexing fiber optic laser induced fluorescence spectroscopy for detection of aflatoxin B1 contaminated pistachio kernels

Toward commercial applications of LED and laser-induced fluorescence techniques for food identity, quality, and safety monitoring: A review

The assessment of food safety and quality is a matter of paramount importance, especially considering the challenges posed by climate change. Convenient, eco-friendly, and non-destructive techniques have attracted extensive attention in the food industry because they can retain food safety and quality. Fluorescence radiation, the process by which fluorophore emits light upon the absorption of ultraviolet or visible light, offers the advantages of high sensitivity and selectivity. The use of excitation-emission matrix (EEM) has been extensively explored in the food industry, but on-site detection of EEMs remain a challenge. To address this limitation, laser-induced fluorescence (LIF) and light emitting diode-induced fluorescence (LED-IF) have been implemented in many cases to facilitate the transition of fluorescence measurements from the laboratory to commercial applications. This review provides an overview of the application of commercially available LIF/LED-IF devices for non-destructive food measurement and recent studies that focus on the development of LIF/LED-IF devices for commercial applications. These studies were categorized into two stages: the preliminary exploration stage, which emphasizes the selection of an appropriate excitation wavelength based on the combination of EEM and chemometrics, and the pre-application stage, where experiments were conducted on scouting with specific excitation wavelength. Although commercially available devices have emerged in many research fields, only a limited number have been reported for use in the food industry. Future studies should focus on enhancing the diversity of test samples and parameters that can be measured by a single device, exploring the application of LIF techniques for detecting low-concentration substances in food, investigating more quantitative approaches, and developing embedded computing devices.

Aflatoxin detection technologies: recent advances and future prospects

Aflatoxins have posed serious threat to food safety and human health. Therefore, it is important to detect aflatoxins in samples rapidly and accurately. In this review, various technologies to detect aflatoxins in food are discussed, including conventional ones such as thin-layer chromatography (TLC), high performance liquid chromatography (HPLC), enzyme linked immunosorbent assay (ELISA), colloidal gold immunochromatographic assay (GICA), radioimmunoassay (RIA), fluorescence spectroscopy (FS), as well as emerging ones (e.g., biosensors, molecular imprinting technology, surface plasmon resonance). Critical challenges of these technologies include high cost, complex processing procedures and long processing time, low stability, low repeatability, low accuracy, poor portability, and so on. Critical discussion is provided on the trade-off relationship between detection speed and detection accuracy, as well as the application scenario and sustainability of different technologies. Especially, the prospect of combining different technologies is discussed. Future research is necessary to develop more convenient, more accurate, faster, and cost-effective technologies to detect aflatoxins.

Deep-red double-clad fiber laser at 717 nm

We report on a double-clad fiber laser operating on the ³P₀ → ³F₄ Pr³⁺ transition (in the deep-red spectral range) pumped by a GaN diode laser at ∼442 nm. It employs a 0.8-mol% PrF₃-doped ZBLAN double-clad fiber with a 7.5-µm core, a double D-shaped inner cladding, and a length of 3.0 m. The laser delivers a maximum output power of 0.71 W at 716.7 nm with a slope efficiency of 9.0% (versus the launched pump power) and a laser threshold of 0.90 W. The laser emission is partially polarized. The laser performance is simulated providing a guideline for watt-level deep-red fiber laser sources.

Selection of characteristic wavelengths using SMA for laser induced fluorescence spectroscopy of power transformer oil

As the core component of the power system, the accurate analysis of its state and fault type is very important for the maintenance and repair of the transformer. The detection method represented by the transformer oil dissolved gas has the disadvantages of complicated processing steps and high operation requirements. Here, laser induced fluorescence (LIF) spectroscopy was applied for the analysis of transformer oil. Specifically, the slime mould algorithm (SMA) was used to select the characteristic wavelengths of the transformer oil fluorescence spectrum, and on this basis, a transformer fault diagnosis model was constructed. First, samples of transformer oil in different states were collected, and the fluorescence spectrum of the transformer oil was obtained with the help of the LIF acquisition system. Then, different spectral pretreatments were performed on the original fluorescence spectra, and it was found that the pretreatment effect of Savitzky-Golay smoothing (SG) was the best. Then, SMA was used to screen the characteristic wavelengths of the fluorescence spectrum, and 137 characteristic wavelengths were screened out to realize the accurate identification of the fluorescence spectrum of the transformer oil. In addition, the advantages of SMA for feature wavelength screening of transformer oil fluorescence spectra were demonstrated by comparing with traditional feature extraction strategies using principal components analysis (PCA). The research results show that it is effective to use SMA to screen the characteristic wavelengths of the LIF spectroscopy of transformer oil and use it for transformer fault diagnosis, which is of great significance for promoting the development of transformer fault diagnosis technology.

Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B1 contaminated (Zea mays L.) seeds: a preliminary study

Cereal seeds safety may be compromised by the presence of toxic contaminants, such as aflatoxins. Besides being carcinogenic, they have other adverse health effects on humans and animals. In this preliminary study, we used two non-invasive optical techniques, optical fiber fluorescence spectroscopy and multispectral imaging (MSI), for discrimination of maize seeds naturally contaminated with aflatoxin B₁ (AFB₁) from the uncontaminated seeds. The AFB₁-contaminated seeds exhibited a red shift of the emission maximum position compared to the control samples. Using linear discrimination analysis to analyse fluorescence data, classification accuracy of 100% was obtained to discriminate uncontaminated and AFB₁-contaminated seeds. The MSI analysis combined with a normalized canonical discriminant analysis, provided spectral and spatial patterns of the analysed seeds. The AFB₁-contaminated seeds showed a 7.9 to 9.6-fold increase in the seed reflectance in the VIS region, and 10.4 and 12.2-fold increase in the NIR spectral region, compared with the uncontaminated seeds. Thus the MSI method classified successfully contaminated from uncontaminated seeds with high accuracy. The results may have an impact on development of spectroscopic non-invasive methods for detection of AFs presence in seeds, providing valuable information for the assessment of seed adulteration in the field of food forensics and food safety.

Quantitative detection of Aflatoxin B1 by subpixel CNN regression

Aflatoxin is a highly toxic substance dispersed in peanuts, which seriously harms the health of humans and animals. In this paper, we propose a new method for aflatoxin B1(AFB1) detection inspired by quantitative remote sensing. Firstly, we obtained the relative content of AFB1 at the sub-pixel level by subpixel decomposition (endmember extraction, nonnegative matrix decomposition). Then we modified the transfer learning models (LeNet5, AlexNet, VGG16, and ResNet18) to construct a deep learning regression network for quantitative detection of AFB1. There are 67,178 pixels used for training and 67,164 pixels used for testing. After subpixel decomposition, each aflatoxin pixel was determined to contain content, and each pixel had 400 hyperspectral values (415-799 nm). The experimental results showed that, among the four models, the modified ResNet18 model achieved the best effect, with R² of 0.8898, RMSE of 0.0138, and RPD of 2.8851. Here, we implemented a sub-pixel model for quantitative AFB1 detection and proposed a regression method based on deep learning. Meanwhile, the modified convolution classification model has high predictive ability and robustness. This method provides a new scheme in designing the sorting machine and has practical value.

Estimating bulk optical properties of AFB1 contaminated edible oils in 300-900 nm by combining double integrating spheres technique with laser induced fluorescence spectroscopy

An optical detection platform based on laser induced spectroscopy and double integrating spheres techniques was developed to obtain absorption (μ_a), reduced scattering coefficients (μ'_s) and fluorescence intensity of oil. The validation experiment carried on liquid phantoms showed that the developed system could achieve high linearity, and the results of spectra analysis indicated that the fluorescence intensity has a significant negative correlation with both μ_a and μ'_s. A total of 1620 oils with six categories were detected. The reason for the difference of fluorescence and μ_a spectra was analyzed by comparing the measured chlorophyll, polyphenol and α-tocopherol contents. Linear discriminant analysis combined with principal component analysis based on fluorescence and μ_a spectra was employed, to calibrate the AFB₁ classification models. The discrimination results manifested that by integrating μ_a with fluorescence signal, the correct classification rate could be improved by more than 10%, and the false negative rate was greatly reduced.

Mycotoxins in Pistachios (Pistacia vera L.): Methods for Determination, Occurrence, Decontamination

The consumption of pistachios (Pistacia vera L.) has been increasing, given their important benefit to human health. In addition to being an excellent nutritional source, they have been associated with chemical hazards, such as mycotoxins, resulting in fungal contamination and its secondary metabolism. Aflatoxins (AFs) are the most common mycotoxins in pistachio and the most toxic to humans, with hepatotoxic effects. More mycotoxins such as ochratoxin A (OTA), fumonisins (FBs), zearalenone (ZEA) and trichothecenes (T2, HT2 and DON) and emerging mycotoxins have been involved in nuts. Because of the low levels of concentration and the complexity of the matrix, the determination techniques must be very sensitive. The present paper carries out an extensive review of the state of the art of the determination of mycotoxins in pistachios, concerning the trends in analytical methodologies for their determination and the levels detected as a result of its contamination. Screening methods based on immunoassays are useful due to their simplicity and rapid response. Liquid chromatography (LC) is the gold standard with new improvements to enhance accuracy, precision and sensitivity and a lower detection limit. The reduction of Aspergillus' and aflatoxins' contamination is important to minimize the public health risks. While prevention, mostly in pre-harvest, is the most effective and preferable measure to avoid mycotoxin contamination, there is an increased number of decontamination processes which will also be addressed in this review.

Fast quantification of total volatile basic nitrogen (TVB-N) content in beef and pork by near-infrared spectroscopy: Comparison of SVR and PLS model

With application of PLS regression and SVR, quantitation models of near infrared diffuse reflectance spectroscopy were established for the first time to predict the content of volatile basic nitrogen (TVB-N) content in beef and pork. Results indicated that the best PLS model based on the raw spectra showed an excellent prediction performance with a high value of correlation coefficient at 0.9366 and a low root-mean-square error of prediction value of 3.15, and none of those pretreatment methods could improve the prediction performance of the PLS model. Moreover, comparatively the model obtained by SVR showed inferior quantitative predictive ability (R = 0.8314, RMSEP = 4.61). Analysis on VIP selected wavelengths inferred amino bond containing compounds and lipid may play important roles in the development of PLS models for TVB-N. Results from this study demonstrated the potential of using NIR spectroscopy and PLS for the prediction of TVB-N in beef and pork while more efforts are required to improve the performance of SVR models.

Biocompatibility nanoprobe of MXene N-Ti3C2 quantum dot/Fe3+ for detection and fluorescence imaging of glutathione in living cells

MXene quantum dots have attracted much attention due to their great optical performance and excellent water solubility. Glutathione (GSH) plays a key role in living cells. In this study, a biocompatibility nanoprobe was prepared for detecting intracellular GSH based on MXene N-Ti₃C₂ quantum dots (N-Ti₃C₂ QDs). The N-Ti₃C₂ QDs act as the fluorescence reporters and the ferric iron (Fe³⁺) as the quenchers based on nonradiative electron-hole annihilation. When Fe³⁺ encounters the amino group of N-Ti₃C₂ QDs, the electrons of N-Ti₃C₂ QDs in the excited state will transfer to the half-filled 3d orbitals of Fe³⁺, leading to the fluorescence quenching of N-Ti₃C₂ QDs. When the N-Ti₃C₂ QDs/Fe³⁺ nanoprobe acts on the cancer cell MCF-7, the abundant GSH in the cancer cells can reduce Fe³⁺ to Fe²⁺, which will restore the fluorescence of N-Ti₃C₂ QDs. The N-Ti₃C₂ QDs/Fe³⁺ nanoprobe displays a high sensitivity for GSH with a detection limit of 0.17 μM in range of 0.5-100 μM. It becomes a promising probe for detecting and showing cellular imaging of GSH in MCF-7 cells. The N-Ti₃C₂ QDs/Fe³⁺ nanoprobe might provide a new way for imaging-guided precision cancer diagnosis.

Recent Advances in Aflatoxins Detection Based on Nanomaterials

Aflatoxins are the secondary metabolites of Aspergillus flavus and Aspergillus parasiticus and are highly toxic and carcinogenic, teratogenic and mutagenic. Ingestion of crops and food contaminated by aflatoxins causes extremely serious harm to human and animal health. Therefore, there is an urgent need for a selective, sensitive and simple method for the determination of aflatoxins. Due to their high performance and multipurpose characteristics, nanomaterials have been developed and applied to the monitoring of various targets, overcoming the limitations of traditional methods, which include process complexity, time-consuming and laborious methodologies and the need for expensive instruments. At the same time, nanomaterials provide general promise for the detection of aflatoxins with high sensitivity, selectivity and simplicity. This review provides an overview of recent developments in nanomaterials employed for the detection of aflatoxins. The basic aspects of aflatoxin toxicity and the significance of aflatoxin detection are also reviewed. In addition, the development of different biosensors and nanomaterials for aflatoxin detection is introduced. The current capabilities and limitations and future challenges in aflatoxin detection and analysis are also addressed.

Highly sensitive and selective aflatoxin B1 biosensor based on Exonuclease I-catalyzed target recycling amplification and targeted response aptamer-crosslinked hydrogel using electronic balances as a readout

The biosensors based on aptamer based stimuli-responsive hydrogels have the characters of high specificity, good stability, portability. Electronic balance is one of the most accurate equipment and can be reached nearly in all labs. Aflatoxin B₁ (AFB₁) is highly toxic and carcinogenic to humans and animals, it is necessary to develop simple and convenient detection method to apply in resource limited area. In this study, a novel strategy for quantitative detection of AFB₁ has been developed by combining the high selectivity and convenient of target-responsive hydrogel and the simple of using electronic balance as readout devices. The AFB₁ target responsive double crosslinked hydrogel has been constructed using linear hyaluronic acid grafted single-stranded DNA complex as the backbone, AFB₁ aptamer and polyethyleneimine as crosslinkers. And platinum nanoparticles (PtNPs) had been embedded in the hydrogel firstly. The present of AFB₁ can bind with the aptamer with high affinity and cause the releasing of aptamer from hydrogel. The addition of Exo I can specifically recognize and cleave the aptamer in AFB₁-aptamer complex, resulting in the releasing of AFB₁, which can react with the hydrogel again, thereby achieving the target cycle. By this means, the hydrogel will collapse and many pre-embedded PtNPs can be released. The transferring of the released PtNPs to a drainage device which contains H₂O₂ can results in the increasing of the internal pressure since the production of oxygen through the catalytic decomposition of H₂O₂ by PtNPs has low solubility. Which will cause the discharging of water from the system and this can be collected and weighed by an electronic balance easily. The weight of water has a linear relationship with AFB₁ concentration. Under 30 min catalytic time, the linear range is 31.2 μg/kg - 6.2 mg/kg with the detection limit of 9.4 μg/kg (S/N = 3). The proposed method was successfully applied to the detection of AFB₁ in peanut samples.

Rapid and sensitive detection of mycotoxins by advanced and emerging analytical methods: A review

Quantification of mycotoxins in foodstuffs is extremely difficult as a limited amount of toxins are known to be presented in the food samples. Mycotoxins are secondary toxic metabolites, made primarily by fungal species, contaminating feeds and foods. Due to the presence in globally used grains, it is an unpreventable problem that causes various acute and chronic impacts on human and animal health. Over the previous few years, however, progress has been made in mycotoxin analysis studies. Easier techniques of sample cleanup and advanced chromatographic approaches have been developed, primarily high-performance liquid chromatography. Few extremely sophisticated and adaptable tools such as high-resolution mass spectrometry and gas chromatography-tandem MS/MS have become more important. In addition, Immunoassay, Advanced quantitative techniques are now globally accepted for mycotoxin analysis. Thus, this review summarizes these traditional and highly advance methods and their characteristics for evaluating mycotoxins.