Combining excitation-emission matrix fluorescence spectroscopy, parallel factor analysis, cyclodextrin-modified micellar electrokinetic chromatography and partial least squares class-modelling for green tea characterization

Identifying the Producer and Grade of Matcha Tea through Three-Dimensional Fluorescence Spectroscopy Analysis and Distance Discrimination

The three-dimensional fluorescence spectroscopy features the advantage of obtaining emission spectra at different excitation wavelengths and providing more detailed information. This study established a simple method to discriminate both the producer and grade of matcha tea by coupling three-dimensional fluorescence spectroscopy analysis and distance discrimination. The matcha tea was extracted three times and three-dimensional fluorescence spectroscopies of these tea infusions were scanned; then, the dimension of three-dimensional fluorescence spectroscopies was reduced by the integration at three specific areas showing local peaks of fluorescence intensity, and a series of vectors were constructed based on a combination of integrated vectors of the three tea infusions; finally, four distances were used to discriminate the producer and grade of matcha tea, and two discriminative patterns were compared. The results indicated that proper vector construction, appropriate discriminative distance, and correct steps are three key factors to ensure the high accuracy of the discrimination. The vector based on the three-dimensional fluorescence spectroscopy of all three tea infusions resulted in a higher accuracy than those only based on spectroscopy of one or two tea infusions, and the first tea infusion was more sensitive than the other tea infusion. The Mahalanobis distance had a higher accuracy that was up to 100% when the vector is appropriate, while the other three distances were about 60-90%. The two-step discriminative pattern, identifying the producer first and the grade second, showed a higher accuracy and a smaller uncertainty than the one-step pattern of identifying both directly. These key conclusions above help discriminate the producer and grade of matcha in a quick, accurate, and green method through three-dimensional fluorescence spectroscopy, as well as in quality inspections and identifying the critical parameters of the producing process.

Handling Variables, via Inversion of Partial Least Squares Models for Class-Modelling, to Bring Defective Items to Non-Defective Ones

In the context of binary class-modelling techniques, the paper presents the computation in the input space of linear boundaries of a class-model constructed with given values of sensitivity and specificity. This is done by inversion of a decision threshold, set with these values of sensitivity and specificity, in the probabilistic class-models computed by means of PLS-CM (Partial Least Squares for Class-Modelling). The characterization of the boundary hyperplanes, in the latent space (space spanned by the selected latent variables of the fitted PLS model) or in the input space, makes it possible to calculate directions that can be followed to move objects toward the class-model of interest. Different points computed along these directions will show how to modify the input variables (provided they can be manipulated) so that, eventually, a computed ‘object’ would be inside the class-model, in terms of the prediction with the PLS model. When the class of interest is that of “adequate” objects, as for example in some process control or product formulation, the proposed procedure helps in answering the question about how to modify the input variables so that a defective object would be inside the class-model of the adequate (non-defective) ones. This is the situation illustrated with some examples, taken from the literature when modelling the class of adequate objects.

Study on Dissolution Characteristics of Excess Sludge by Low-Temperature Thermal Hydrolysis and Acid Production by Fermentation

To investigate the dissolution characteristics of low-temperature thermal pretreatment conditions and the process of sludge fermentation to produce acid, the influence of thermal pretreatment temperature on the dissolution of excess sludge organic composition and the mechanism of cell crushing of sludge thermal pretreatment were analyzed by an experimental method, and the performance of acid production was explored by sludge fermentation after pretreatment at different temperatures. The performance of acid production by sludge fermentation after pretreatment at different temperatures was measured. The results proved that the soluble chemical oxygen demand (SCOD) shows the largest increase in dissolution rate (11.92%) at 70 °C and in dissolution quantity (6518.33 mg/L) at 90 °C. However, at 80 °C, the solubility of total organic carbon (TOC) is the highest (3224.47 mg/L), and at 70 °C, the best dissolution conditions for soluble carbohydrate (SC) and soluble protein (SP) reached 340.07 and 80.92 mg/L, respectively. The degree of sludge breaking starts to increase at 70 °C. Correlation analysis shows that dissolved organic matter is mainly derived from the cell wall and intracellular material and SP is mainly derived from intracellular material. Excitation-emission matrix spectra and parallel factor analysis (EEM-PARAFAC) divides the sludge dissolved organic matter (DOM) into five fluorescent components, including C1 (318/366) tyrosine, C2 (418/470) UVA humic acid, C3 (282/334) tryptophan substances, C4 (322/430) UVC humic acids, and C5 (314, 382, 454/526) UVA humic substances. Fermentation acid production experiment shows that the peak concentration is highest at 80 °C, the arrival time is 2 days, and the acid production type is butyric acid fermentation. Thus, it is proved that low-temperature thermal pretreatment promotes the process of acid-producing fermentation and has no effect on the type of fermentation. The optimal condition for hydrolytic dissolution and acid production under low-temperature thermal pretreatment is 80 °C.