Comparison of three algorithms for the baseline correction of hyphenated data objects

Application and performance enhancement of FAIMS spectral data for deep learning analysis using generative adversarial network reinforcement

When using High-field asymmetric ion mobility spectrometry (FAIMS) to process complex mixtures for deep learning analysis, there is a problem of poor recognition performance due to the lack of high-quality data and low sample diversity. In this paper, a Generative Adversarial Network (GAN) method is introduced to simulate and generate highly realistic and diverse spectral for expanding the dataset using real mixture spectral data of 15 classes collected by FAIMS. The mixed datasets were put into VGG and ResNeXt for testing respectively, and the experimental results proved that the best recognition effect was achieved when the ratio of real data to generated data was 1:4: where accuracy improved by 24.19 % and 6.43 %; precision improved by 23.71 % and 6.97 %; recall improved by 21.08 % and 7.09 %; and F1-score improved by 24.50 % and 8.23 %. The above results strongly demonstrate that GAN can effectively expand the data volume and increase the sample diversity without increasing the additional experimental cost, which significantly enhances the experimental effect of FAIMS spectral for the analysis of complex mixtures.

Large volume headspace GC/MS analysis for the identification of volatile compounds relating to seafood decomposition

Decomposition in seafood products in the United States is monitored by the Food and Drug Administration (FDA) laboratories using sensory testing, which requires highly trained analysts. A large‐volume headspace (LVHS) gas chromatography/mass spectrometry (GC/MS) method was developed to generate analytical results that can be directly compared to sensory evaluation. Headspace vapor was withdrawn from a 1‐L vial containing 50 g seafood sample using a large volume headspace autosampler. Various volatile compounds were collected simultaneously. Analytes were preconcentrated by a capillary column trapping system and then sent through a cryo‐focuser mounted onto the GC injector. A selected ion monitoring (SIM) MS acquisition method was used to selectively monitor 38 compounds of interest. Samples of red snapper, croaker, weakfish, mahi‐mahi, black tiger shrimp, yellowfin tuna, and sockeye salmon that have been assessed and scored by an FDA National Seafood Sensory Expert (NSSE) were used for method performance evaluation. Characteristic compounds potentially associated with seafood quality deterioration for each seafood species were identified by quantitative analysis using pooled matrix‐matched calibrations and two‐sample t‐test statistical analysis. Classification of fresh and decomposed samples was visualized on the analysis of variance (ANOVA)–principal component analysis (PCA) score plots. The results determined that the LVHS‐GC/MS technique appeared promising as a screening tool to identify compounds representative of sensory analysis.

A quantitative reliability metric for querying large database

A redesigned quantitative reliability metric based on the F-distribution (QRMf) is reported for evaluating the reliability of library search. The QRMf provides orthogonal information to the comparison metric (e.g., dot product) and yields a probabilistic result. An intralibrary search can be considered as an idealized search because the top hit, i.e., the closest matching object, will match perfectly. If the search of an unknown object yields the same hit list as the intralibrary search, it would indicate good reliability. For each object in the hit list, a QRMf compares the order of an intralibrary and interlibrary search results and calculates a variance of interlibrary similarity metrics between the records of the intralibrary search and records in the corresponding positions of the interlibrary search. This variance that measures the discordance of the intra and interlibrary search can simply be compared to the variance of the similarity metrics within the interlibrary search results. The ratio of these variances follows an F-distribution that can be used to determine if the discordance is statistically significant and generates the probability based on the cumulative distribution function. The QRMf works for both similarity and dissimilarity and can be used for any queried object and comparison metric that is searched against a database. In this work, the QRMf was used along with the dot product similarity to query the mass spectra of novel synthetic opioids measured by gas chromatography-mass spectrometry (GC/MS). An automated pipeline was devised that used a basis set correction to assist peak detection. The basis was constructed by mass spectra obtained from the blank measurement preceding the analytical run to remove interferences from column bleed and septum degradation. After peak detection, the pipeline applied multivariate curve resolution to the chromatographic peak window to remove background components from the mass spectra. The corrected mass spectra were searched against a customized library for identification. The QRMf can be used along with the similarity metric to detect misidentifications and assist in finding the correct identification when it is not the closest match.

A general-purpose signal processing algorithm for biological profiles using only first-order derivative information

BACKGROUND

Automatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences.

RESULTS

We developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted.

CONCLUSIONS

We have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.

Continuous background correction using effective points selected in third-order minima segments in low-cost laser-induced breakdown spectroscopy without intensified CCD

This work presents a method that can automatically estimate and remove varying continuous background emission for low-cost laser-induced breakdown spectroscopy (LIBS) without intensified CCD. The algorithm finds all third-order minima points in spectra and uses these points to partition the spectra into multiple subintervals. The mean value is then used as a threshold to select the effective points for the second-order minima in each subinterval. Finally, a linear interpolation method is used to realize extension of these effective points and complete fitting of the background using polynomials. Using simulated and real LIBS spectra with different complexities examine the validity of proposed algorithm. Additionally, five elements of five standard cast iron alloy samples are calibrated and improved very well after background removal. The results successfully prove the validity of the background correction algorithm.

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

Background correction in separation techniques hyphenated to high-resolution mass spectrometry - Thorough correction with mass spectrometry scans recorded as profile spectra

Separation techniques hyphenated with high-resolution mass spectrometry have been a true revolution in analytical separation techniques. Such instruments not only provide unmatched resolution, but they also allow measuring the peaks accurate masses that permit identifying monoisotopic formulae. However, data files can be large, with a major contribution from background noise and background ions. Such unnecessary contribution to the overall signal can hide important features as well as decrease the accuracy of the centroid determination, especially with minor features. Thus, noise and baseline correction can be a valuable pre-processing step. The methodology that is described here, unlike any other approach, is used to correct the original dataset with the MS scans recorded as profiles spectrum. Using urine metabolic studies as examples, we demonstrate that this thorough correction reduces the data complexity by more than 90%. Such correction not only permits an improved visualisation of secondary peaks in the chromatographic domain, but it also facilitates the complete assignment of each MS scan which is invaluable to detect possible comigration/coeluting species.

Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics

Economically motivated adulteration (EMA) of lemon juice was detected by LC-MS and principal component analysis (PCA). Twenty-two batches of freshly squeezed lemon juice were adulterated by adding an aqueous solution containing 5% citric acid and 6% sucrose to pure lemon juice to obtain 30%, 60% and 100% lemon juice samples. Their total titratable acidities, °Brix and pH values were measured, and then all the lemon juice samples were subject to LC-MS analysis. Concentrations of hesperidin and eriocitrin, major phenolic components of lemon juice, were quantified. The PCA score plots for LC-MS datasets were used to preview the classification of pure and adulterated lemon juice samples. Results showed a large inherent variability in the chemical properties among 22 batches of 100% lemon juice samples. Measurement or quantitation of one or several chemical properties (targeted detection) was not effective in detecting lemon juice adulteration. However, by using the LC-MS datasets, including both chromatographic and mass spectrometric information, 100% lemon juice samples were successfully differentiated from adulterated samples containing 30% lemon juice in the PCA score plot. LC-MS coupled with chemometric analysis can be a complement to existing methods for detecting juice adulteration.

Application of gas chromatography/flame ionization detector-based metabolite fingerprinting for authentication of Asian palm civet coffee (Kopi Luwak)

Development of authenticity screening for Asian palm civet coffee, the world-renowned priciest coffee, was previously reported using metabolite profiling through gas chromatography/mass spectrometry (GC/MS). However, a major drawback of this approach is the high cost of the instrument and maintenance. Therefore, an alternative method is needed for quality and authenticity evaluation of civet coffee. A rapid, reliable and cost-effective analysis employing a universal detector, GC coupled with flame ionization detector (FID), and metabolite fingerprinting has been established for discrimination analysis of 37 commercial and non-commercial coffee beans extracts. gas chromatography/flame ionization detector (GC/FID) provided higher sensitivity over a similar range of detected compounds than GC/MS. In combination with multivariate analysis, GC/FID could successfully reproduce quality prediction from GC/MS for differentiation of commercial civet coffee, regular coffee and coffee blend with 50 wt % civet coffee content without prior metabolite details. Our study demonstrated that GC/FID-based metabolite fingerprinting can be effectively actualized as an alternative method for coffee authenticity screening in industries.