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For the authentication of powdered spices, the possible impact of the natural variations of adulterants on classification or prediction models is always ignored, and research in this area is still quite scarce. This study takes the application of front-face synchronous fluorescence spectroscopy (FFSFS) to the rapid and non-destructive authentication of cumin powder adulterated with ground peanut shells and maize flour as an example to show that how the natural variations of adulterants affect model prediction. Using three samples of each adulterant, two modes of sample set were designed for comparison. Mode 1 used the first adulterant for calibration and the other two for external validation. Mode 2 employed two adulterants in training and the third for external validation. The classification of adulterants by principal component analysis coupled with linear discriminant analysis (PCA–LDA) showed that mode 2 had a higher prediction rate (82%) in external validation than mode 1 (63%). For quantification, prediction models were built by partial least square (PLS) regression, and were validated by both cross- and external validation. The result of mode 2 was better than that of mode 1, with the determination coefficients of prediction (R p 2) greater than 0.93, the root mean square error of prediction (RMSEP) < 4.2% and residual predictive deviation (RPD) at least 2.7. This study demonstrates the necessity of the consideration of the natural variations of adulterants when constructing a robust model for adulteration detection. [Display omitted] • Front-face fluorescent property of cumin powder is investigated. • Its emission is dissimilar to those of ground peanut shells and maize flour. • Flavonoids, phenolic acids and tryptophan contribute to the distinction. • Simultaneous detection of dual adulterants in cumin powder by PLS2. • Effect of natural variation of adulterants on prediction model is evaluated. [ABSTRACT FROM AUTHOR]

Synchronous front-face fluorescence spectroscopy has been developed for the discrimination of used frying oil (UFO) from edible vegetable oil (EVO), the estimation of the using time of UFO, and the determination of the adulteration of EVO with UFO. Both the heating time of laboratory prepared UFO and the adulteration of EVO with UFO could be determined by partial least squares regression (PLSR). To simulate the EVO adulteration with UFO, for each kind of oil, fifty adulterated samples at the adulterant amounts range of 1–50% were prepared. PLSR was then adopted to build the model and both full ( leave-one-out ) cross-validation and external validation were performed to evaluate the predictive ability. Under the optimum condition, the plots of observed versus predicted values exhibited high linearity ( R 2 > 0.96). The root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) were both lower than 3%. [ABSTRACT FROM AUTHOR]

[Display omitted] • First observation of fluorescence quenching between solid foods. • Fluorescence quenching is proven to be induced by competitive absorption. • Competitive absorption occurs between phenolic acids and curcumin. • Fast and non-invasive analysis of maize flour in turmeric powder by FFSFS and PLS. • FFSFS offers a new strategy for composition analysis of solid food blends. Fluorescence quenching induced by competitive absorption between different components of solid foods was observed for the first time. By using front-face synchronous fluorescence spectroscopy (FFSFS) and fluorescence titration, competitive absorption between maize flour and turmeric powder was proven to occur between phenolic acids in maize flour and curcumin in turmeric powder. FFSFS was applied for the rapid and non-destructive determination of maize flour adulterated in turmeric powder. Prediction models were constructed by partial least square (PLS) regression based on unfolded total synchronous fluorescence spectra, and were validated by five-fold cross-validation and external validation, with the determination coefficient of prediction (R p 2) greater than 0.95, root mean square error of prediction (RMSEP) < 6%, relative error of prediction (REP) < 15% and residual predictive deviation (RPD) greater than 5. The limit of detection (LOD) of maize flour was approximately 9%. In addition, most relative errors for test samples were from −20% to 20%. [ABSTRACT FROM AUTHOR]

Quinoa is a naturally gluten-free pseudocereal receiving growing attentions worldwide. Herein, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of quinoa flour adulteration with maize and soybean flours. The cases of both single and dual adulterants (6–50 wt%) were investigated. For single adulteration with either maize or soybean flour, principal component analysis coupled with linear discriminant analysis (PCA–LDA) was used for preliminary qualitative discrimination. Then first-order prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by five-fold cross-validation and external validation. For dual adulterants, a general PLS2 model for the simultaneous determination of maize and soybean flours was developed, which produced suitable results, with the determination coefficient of prediction (R p 2) > 0.9, root mean square error of prediction (RMSEP) < 5% and residual predictive deviation (RPD) > 3. The limits of detection (LODs) are 9.0% and 6.9% for maize flour and soybean flour, respectively. Furthermore, most spike recoveries are in the range of 80–120%. This study not only has important applications in the authentication of quinoa flour adulteration but may also shed light on the quantitative component analysis of other cereal flour mixtures from different botanical origins. • Front-face synchronous fluorescence of quinoa flour is presented. • Its emission is distinct from those of maize and soybean flours. • Quinoa flavonoids contribute most to the distinction, followed by tryptophan. • Front-face fluorescence affords non-destructive detection of its adulteration. • Simultaneous determination of maize and soybean flours can be rapidly achieved. [ABSTRACT FROM AUTHOR]

[Display omitted] • Front-face fluorescence of ground black and white pepper is observed. • Their emissions are distinct from those of several possible adulterants. • Piperine in pepper and tryptophan in adulterants yield the distinction. • Simultaneous detection of multiple adulterants in ground pepper is feasible. • Rapid and non-destructive analysis of piperine is achieved as well. Based on the distinct fluorescence of piperine and tryptophan, and their different profiles in pepper and several possible adulterants, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of ground black pepper adulterated with papaya seed powder and buckwheat flour, and ground white pepper adulterated with whole wheat and maize flours. For either single adulterant or dual adulterants in the range of 10–40% w/w, prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by both five-fold cross-validation and external validation. The built PLS2 models produced suitable results, with most of the determination coefficients of prediction (R p 2) greater than 0.8, the root mean square error of prediction (RMSEP) < 5% and residual predictive deviation (RPD) greater than 2. The limits of detection (LODs) were 11.1, 5.5, 10.6 and 12.0% for papaya seed powder, buckwheat, whole wheat and maize flours, respectively. Most relative prediction errors for simulated blind samples were within ± 30%. Besides, piperine in ground black and white pepper was also determined with acceptable PLS results. [ABSTRACT FROM AUTHOR]

The authentication of traditional herbal medicines in powder form is of great significance, as they are always of high values but vulnerable to adulteration. Based on the distinct fluorescence of protein tryptophan, phenolic acids and flavonoids, front-face synchronous fluorescence spectroscopy (FFSFS) was applied for the fast and non-invasive authentication of Panax notoginseng powder (PP) adulterated with the powder of rhizoma curcumae (CP), maize flour (MF) and whole wheat flour (WF). For either single or multiple adulterants in the range of 5–40% w /w, prediction models were built based on the combination of unfolded total synchronous fluorescence spectra and partial least square (PLS) regression, and were validated by both five-fold cross-validation and external validation. The constructed PLS2 models simultaneously predicted the contents of multiple adulterants in PP and gave suitable results, with most of the determination coefficients of prediction (R p 2) >0.9, the root mean square error of prediction (RMSEP) no >4% and residual predictive deviation (RPD) >2. The limits of detections (LODs) were 12.0, 9.1 and 7.6% for CP, MF and WF, respectively. All the relative prediction errors for simulated blind samples were between −22% and + 23%. FFSFS offers a novel alternative to the authentication of powdered herbal plants. [Display omitted] • Front-face fluorescence of Panax notoginseng powder is observed and utilized. • Simultaneous detection of multiple adulterants in Panax notoginseng powder by PLS2. • Protein tryptophan, phenolic acids and flavonoids contribute to the detection. • Front-face fluorescence affords rapid and non-destruction drug authentication. [ABSTRACT FROM AUTHOR]

• A novel, portable, and automatic detection device for chemical sensing was constructed. • An inexpensive, disposable, 3D printed microfluidic chip was designed and applied for detection with chemical formulations. • A novel and unique fluorescence difference pattern were produced based on cross-responsive mechanism for specificity recognition. In the current study, we report here the design, fabrication and functional verification of a novel, portable and automatic dual-readout device for carbamate pesticides detection applications with low detection limits. This device integrated a 3D printed, low cost and disposable microfluidic chip to deliver nanomaterials and pesticides samples. Based on the cross-responsive mechanism and the agglomeration effect of AuNPs, two model signals (fluorescence and color change, dual-readout) were achieved from the response between pesticides and sensing materials. Simultaneously, a novel and unique fluorescence difference patterns (spectra-fingerprint) were produced based on cross-responsive mechanism for specificity recognition. The difference maps including color difference image and fluorescence difference spectra presented good selectivity among carbamate pesticides (six kinds of carbamate pesticides were successfully distinguished) or other categories in low concentration. By use of this dual-signal model, the device could achieve the lowest detectable concentrations for carbamate pesticides at ppb level with the practicability, reproducibility and stability estimated. Importantly, with the features of integration and instrumentation, the detector could be further employed for the home-based testing and environmental monitoring. [ABSTRACT FROM AUTHOR]