Variable Elimination Research Articles

Online Detection of Dry Matter in Potatoes Based on Visible Near-Infrared Transmission Spectroscopy Combined with 1D-CNN

More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R2P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R2P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.

Correlates of intention to screen for cervical cancer among adult women in Kyotera District, Central Uganda: a community based cross-sectional study

IntroductionCervical cancer continues to pose a major public health challenge in low-income countries. Cervical cancer screening programs enable early detection and effectively reduce the incidence of cervical cancer as well as late-stage diagnosis and mortality. However, screening uptake remains suboptimal in Uganda. This study assessed correlates of intention to screen for cervical cancer among women in the Kyotera district of Central Uganda.MethodsWe analyzed cross-sectional data collected to determine the effectiveness of community audio towers (CATs) as a modality of health communication to support cervical cancer prevention. Women (n = 430) aged 21–60 years without a prior history of cervical cancer screening were surveyed about demographics, sources of health information and cervical cancer screening intentions in 2020. We used generalized linear modelling with modified Poisson regression and backwards variable elimination to identify adjusted prevalence ratios and 95% confidence intervals (CI) to determine factors associated with intention to screen for cervical cancer.ResultsHalf (50.2%) of the participants had intentions to screen for cervical cancer within twelve months and 26.5% had moderate knowledge about cervical cancer. Nearly half (46.0%) considered themselves at risk of cervical cancer. Compared to residents who primarily received their health information from social media and radio, participants who received health information primarily from CATs (aPR:0.64, 95% CI:0.52–0.80, p < 0.001) and TV (aPR:0.52, 95% CI:0.34–0.82, p = 0.005) had a lower prevalence of intention to screen for cervical cancer. The prevalence of intentions to screen for cervical cancer in twelve months was higher among those resided in town councils (aPR:1.44, 95% CI:1.12–1.86, p = 0.004) compared to rural areas, and higher among those who considered themselves to be at risk of cervical cancer (aPR:1.74, 95% CI:1.28–2.36, p < 0.001) compared to those who did not.ConclusionsWe found suboptimal prevalence of intentions to screen for cervical cancer among women in central Uganda. Additional research and implementation projects are needed to increase cervical cancer screening. Targeting risk perceptions and behavioral approaches to increase intentions could be effective in future intervention work. Based on urban-rural differences, additional work is needed to support equitable sharing of information to support cancer prevention messaging; CATs and TV may best help reach those with lower intentions to screen based on our research.

Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

Quantitative detection of aging damage of solid propellant based on frequency impedance spectroscopy combined with CARS‐SVM algorithm

AbstractSolid propellant, as the energy source for solid rocket engines, it is of great significance to achieve accurate quantitative detection of aging damage of solid propellant. In this paper, a novel approach based on frequency impedance spectroscopy impedance combined with CARS‐SVM algorithm was proposed. First, the temperature, humidity, and pressure of environmental information around the solid rocket motor were sampled, and then the impedance at corresponding frequencies of the propellant was obtained by AD5933 chip. Second, the processed experimental data were subjected to abnormal sample detection before further variables selection using uninformative variables elimination (UVE) competitive adaptive reweighted sampling (CARS), respectively. Finally, support vector machine (SVM), UVE‐SVM and CARS‐SVM quantitative calibration methods were established. The results showed that the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) of CARS‐SVM model were 0.9919, 0.7540, and 0.0480, respectively. Therefore, the results prove that impedance of solid propellant combined with CARS‐SVM model can effectively achieve high precision quantitative detection of aging damage of solid propellant, which lays a new method for the application of solid propellants aging damage in the online quantitative detection.

Causal Unit Selection using Tractable Arithmetic Circuits

The unit selection problem aims to find objects, called units, that optimize a causal objective function which describes the objects' behavior in a causal context (e.g., selecting customers who are about to churn but would most likely change their mind if encouraged). While early studies focused mainly on bounding a specific class of counterfactual objective functions using data, more recent work allows one to find optimal units exactly by reducing the causal objective to a classical objective on a meta-model, and then applying a variant of the classical Variable Elimination (VE) algorithm to the meta-model---assuming a fully specified causal model is available. In practice, however, finding optimal units using this approach can be very expensive because the used VE algorithm must be exponential in the constrained treewidth of the meta-model, which is larger and denser than the original model. We address this computational challenge by introducing a new approach for unit selection that is not necessarily limited by the constrained treewidth. This is done through compiling the meta-model into a special class of tractable arithmetic circuits that allows the computation of optimal units in time linear in the circuit size. We finally present empirical results on random causal models that show order-of-magnitude speedups based on the proposed method for solving unit selection.

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

Superacute onset of Guillain-Barré syndrome after elective spinal surgery: A case report and literature review.

Guillain-Barré syndrome (GBS) epitomizes an acute peripheral neuropathy hallmarked by an autoimmune retort directed at the myelin sheath enwrapping peripheral nerves. While it is widely acknowledged that a majority of GBS patients boast a history of antecedent infections, the documentation of postoperative GBS occurrences is progressively mounting. Drawing upon an exhaustive compendium of recent case reports, the disease's inception spans a gamut from within 1 hour to 1.2 years. At this juncture, we proffer a singular case: an instance involving a 51-year-old gentleman who underwent lumbar spine surgery, only to encounter immediate debilitation of limb and respiratory musculature. Post elimination of variables linked to anesthetic agents, encephalon, and spinal cord pathologies, a potent suspicion of superacute GBS onset emerged. Subsequent to immunoglobulin therapy, plasmapheresis, and adjunctive support, the patient's ultimate demise became manifest. No progress was found to date. Given GBS's potential to instigate paralysis, respiratory collapse, and autonomic nervous system aberrations, alongside other pernicious sequelae, coupled with the exceptional rarity of the temporal onset in this particular instance, it undeniably proffers an imposing conundrum for anesthetists in the realm of differential diagnosis and therapeutic conduct. During the postoperative convalescence phase under anesthesia, should the patient evince deviant limb musculature vigor and compromised respiratory sinews, the prospect of GBS must not be consigned to oblivion. Precision in diagnosis conjoined with apt therapeutic measures could well be the harbinger of a divergent denouement for the afflicted patient.

Abstract PO5-02-02: LyKi1: a highly predictive immune-based score of pathological response to chemotherapy in luminal breast cancer

Abstract Background: Identifying which luminal breast cancer (BC) patients will benefit from (neo)adjuvant chemotherapy still remains a challenge. Efficient predictive tests and genomic signatures are still lacking. Because neoadjuvant chemotherapy (NACT) constitutes an in vivo chemotherapy-sensitivity test, we used the pre-treatment core biopsies to identify predictive factors of pathological response in luminal HR+/Her2- BC. Analyses on sTILS and TIL subtypes (CD3, CD4, CD8, CD20) are rare in luminal BC while a predictive value has been reported in TNBC and Her-2 positive BC. Patients and Methods: We performed a retrospective analysis of 211 patients treated with NACT followed by surgery for a T0-T4 luminal (HR+/Her-2-) breast cancer. Association of anthracyclin and taxane was used in 99% of cases. Median age was 48.4 yo (28 – 76). Initial clinical stage was: I: 0.5%, II: 67%; III: 33%. Biopsy analyses were the followings: NST carcinomas: 90%; luminal B: 82%; E&amp;E grade I/II/III: 10%/57%/32%; KI-67 &amp;gt; 20%: 74%. After NACT, RCB 0-1 was observed in 32 cases (15.2%) and pCR in 19 (9.0 %) cases. All pre-therapeutic biopsies were reviewed for pathological factors, blinded to the patients’ outcomes. Clinico-pathological analyses were performed using standard methods. sTILs were estimated on H&amp;E slides. Intratumoral CD3, CD4, CD8 and CD20 infiltrates were carried out by an independent laboratory using a machine learning model for automated and quantitative evaluation based on digital IHC stains. Correlation between sTILs and TIL subtypes were assessed by Spearman’s coefficient. Predictive factors of RCB 0/1 were explored using unconditional logistic regression analysis. After selection on univariate analysis (p&amp;lt; 0.05), multivariate prognostic models were developed using stepwise backward variable elimination process and compared using area under the curve (AUC). Results: sTILs levels ≥ 10% were observed in 15.2% of biopsies. 42 % of tumors had ≥ 10 % CD3-positive cells, 46% for CD4, 38% for CD8, and 8% for CD20. A high level of correlation was observed between these different markers (Spearman’s coefficient ≥ 0.73 for each comparison). By univariate analysis, significant factors (p&amp;lt; 0.05) associated with RCB 0-1 were: age, mitotic index (1 vs 2 vs 3), aneuploidy (1-2 vs 3), differentiation (1-2 vs 3), E&amp;E grade (1-2 vs 3), mitotic count, Ki-67, Magee equation 3, phenotypic group (luminal A vs B), ER, sTILs and each TIL subtype. Age, E&amp;E grade, Ki-67, Magee equation 3, sTILs and all TIL subtypes were selected for multivariate analysis. As TIL subtypes were highly correlated, different predictive models could be performed. The model we selected (called LyKi1) was constructed based on CD8 [OR 2.24 (95%CI 1.55-3.25; p&amp;lt; 10-4)], E&amp;E grade [OR 3.00 (95%CI 1.22-7.42; p=0.017)] and Ki-67 [OR 1.74 (95%CI 1.09-2.76; p=0.019)]. It was highly significantly associated with RCB 0-1, with an AUC value of 0.856 (95%CI 0.756-0.916). For instance, LyKi1 could isolate a group of 30% of patients that achieved RCB 0-1 in 39.7% of cases vs 4.7% for the 70% of patients with lowest LyKi1 scores. pCR rates were 25% and 2%, respectively. Importantly, prediction value of LyKi1 remained very high if restricted to luminal B tumors (AUC 0.817; 95% CI: 0.741-0.894). Ability to predict pCR was also very high (AUC 0.837 (95%CI: 0.750-0.924) in this phenotype). Conclusion: This retrospective analysis clearly highlights the important predictive role of sTILs and TIL subpopulations for the response to an anthracyclin-taxane based regimen in a neoadjuvant setting for luminal breast cancer. Within this cohort, LyKi1 score (combining CD8, grade and Ki-67) has a very high value to predict pathological response to NACT in luminal breast cancer. Importantly, such score has to be validated in a multicentric prospective cohort and investigated in adjuvant setting. Citation Format: Marc Debled, Hugo Deboissy, Coralie Cantarel, Chloé Delfour, Léonie Alran, Marion Fournier, Nathalie Quenel-Tueux, Valérie Velasco, Foucault Chammings, Véronique Brouste, Monica Arnedos, Gaétan MacGrogan. LyKi1: a highly predictive immune-based score of pathological response to chemotherapy in luminal breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-02-02.

Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds.

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into "Caciocavallo Podolico" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.

Categorization of continuous covariates and complex regression models—friends or foes in intersectionality research

ObjectivesTo reduce health inequities, it is important to identify intersections in characteristics of individuals subject to privilege or disadvantage. Different proposals for that have recently been published. One approach (1) considers models specified with first- and all second-order effects and another (2) the stratification based on multiple covariates; both categorize continuous covariates. A simulation study was conducted in order to review both methods with regard to identification of intersections showing true differences, rate of false-positive results, and generalizability to independent data compared to an established approach (3) of backward variable elimination according to Bayesian information criterion (BE-BIC) combined with splines. Study Design and SettingR software has been used to simulate the covariates age, sex, body mass index, education, and diabetes to examine their association with a continuous frailty score for osteoporosis using multiple linear regression. In setting 1, none of the covariates was associated with the frailty score, that is, only noise is present in the data. In setting 2, the covariates age, sex, and their interaction were associated with the frailty score, such that only females above 55 years formed an intersection associated with an increased frailty score. All approaches were compared under varying sample sizes (N = 200–3000) and signal-to-noise ratios (SNRs, 0.5–4) in 1000 replications. For model evaluation, bootstrap resampling was used. The models were fitted in internal learning data and then used to predict outcomes in the internal validation data. The mean squared error (MSE) was used for comparison and the frequency of false-positive findings calculated. ResultsIn setting 1, approaches 1 and 2 generated spurious effects in more than 90% of simulations across all sample sizes. In a smaller sample size, approach 3 (BE-BIC) selected 36.5% of the correct model, in larger sample size in 89.8% and always had a lower number of spurious effects. MSE in independent data was generally higher for approaches 1 and 2 when compared to 3. In setting 2, approach 1 selected most frequently the correct interaction but frequently showed spurious effects (>75%). Across all sample sizes and SNR, approach 3 generated least often spurious results and had lowest MSE in independent data. ConclusionCategorization of continuous covariates is detrimental to studies on intersectionality. Due to high and unrestricted model complexity, such approaches are prone to spurious effects and often lack interpretability. Approach 3 (BE-BIC) is considerably more robust against spurious findings, showed better generalizability to independent data, and can be used with most statistical software. For intersectionality research, we consider it most important to describe relevant differences between intersections and to avoid nonreproducible and spurious findings.

Rapid Measurement of Total Saponins, Mannitol, and Naringenin in Dendrobium officinale by Near-Infrared Spectroscopy and Chemometrics.

Dendrobium officinale has drawn increasing attention as a dual-use plant with herbal medicine and food applications. The efficient quality evaluation of D. officinale is essential to ensuring its nutritional and pharmaceutical value. Given that traditional analytical methods are generally time-consuming, expensive, and laborious, this study developed a rapid and efficient approach to assess the quality of D. officinale from different geographical origins by near-infrared (NIR) spectroscopy and chemometrics. Total saponins, mannitol, and naringenin were utilized as quality indicators. Two wavelength selection methods, namely, uninformative variable elimination and competitive adaptive reweighted sampling (CARS), were utilized to enhance the prediction accuracy of the quantification model. Moreover, multiple spectral pretreatment methods were applied for model optimization. Results indicated that the partial least squares (PLS) model constructed based on the wavelengths selected by CARS exhibited superior performance in predicting the contents of the quality indicators. The coefficient of determination (RP2) and root mean square error (RMSEP) in the independent test sets were 0.8949 and 0.1250 g kg-1 for total saponins, 0.9664 and 0.2192 g kg-1 for mannitol, and 0.8570 and 0.003159 g kg-1 for naringenin, respectively. This study revealed that NIR spectroscopy and the CARS-PLS model could be used as a rapid and accurate technique to evaluate the quality of D. officinale.

Design of printing ink spectral collection system and research on ink proportion prediction method

In spectral-based ink content measurement, improving the accuracy of the spectral collection system and reducing the algorithm complexity of the ink content measurement model are important aspects of research. This study designs a spectral collection system with an M-type Czerny–Turner optical path structure, uses a third-order polynomial fitting method for calibration analysis, collects the spectral of the standard color card and denoises it through Savitzky–Golay convolution smoothing, and establishes a functional relationship between the spectral and the logarithm of ink content. Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighting Sampling are used to perform comparative analysis on feature wavelength extraction, and a prediction model for printing ink content is established. Experimental results show that the R2 of ink C, ink M, and ink Y are 0.9981, 0.9975, and 0.9892, respectively, and the root mean square error is 0.0134, 0.0153, and 0.0317, respectively, showing good performance in ink prediction accuracy.

Dapsone – Reinventing the Wheel or Rekindling Possibilities?

Dapsone, once a fabric dye, is a versatile pharmaceutical for treating diseases like leprosy, malaria, and HIV-AIDS-related pneumonia. Discovered in 1908 but not utilized for its antimicrobial properties until the 1930s, dapsone faced initial setbacks due to toxicity, prompting the development of a safer derivative, Promin. Chemically, dapsone's lipid-soluble nature allows for extensive distribution throughout the body and involves complex metabolism, with a variable elimination half-life. Its clinical efficacy is due to its bacteriostatic action, inhibiting dihydrofolic acid synthesis, and its anti-inflammatory effects on neutrophils. Dapsone's dosing is tailored to the individual's condition and is approved for various dermatological conditions. However, its use is limited by contraindications in certain anemic conditions and potential side effects such as hemolytic anemia and methemoglobinemia, necessitating careful monitoring and management strategies to mitigate risks. Despite these challenges, dapsone's broad therapeutic utility and ongoing research into its mechanisms maintain its status as a significant medical therapy.

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy.

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

Accurate identification of methanol and ethanol gasoline types and rapid detection of the alcohol content using effective chemical information

Methanol and ethanol gasoline are two emerging clean energy sources with different characteristics. To achieve the qualitative identification and quantitative analysis of the alcohols present in methanol and ethanol gasoline, effective chemical information (ECI) models based on the characteristic spectral bands of the near-infrared (NIR) spectra of the methanol and ethanol molecules were developed using the partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) algorithms. The ECI model was further compared with models built from the full wavenumber (Full) spectra, variable importance in projection (VIP) spectra, and Monte Carlo uninformative variable elimination (MC-UVE) spectra to determine the predictive performance of ECI model. Among the various qualitative identification models, it was found that the ECI-PLS-DA model, which is built using the differences in molecular chemical information between methanol and ethanol, exhibited sensitivity, specificity and accuracy values of 100%. The ECI-PLS-DA model accurately identified methanol gasoline and ethanol gasoline with different contents. In the quantitative analysis model for methanol gasoline, the methanol gasoline and ethanol gasoline ECI-PLS models exhibited the smallest root mean squared error of predictions (RMSEP) of 0.18 and 0.21% (v/v), respectively, compared to the other models. Meanwhile, the F-test and T-test results revealed that the NIR method employing the ECI-PLS model showed no significant difference compared to the standard method. Compared with other spectral models examined herein, the ECI model demonstrated the highest recognition success and determination accuracy. This study therefore established a highly accurate and rapid determination model for the qualitative identification and quantitative analysis based on chemical structures. It is expected that this model could be extended to the NIR analysis of other physicochemical properties of fuel.

Development of near-infrared spectroscopy calibration model and monitoring software: For monitoring hexamethylenetetramine concentration in hexamethylenetetramine–acetic acid solution

In recent years, near-infrared spectroscopy has been increasingly used in chemical engineering processes. However, accurately and in real-time monitoring concentrations remains highly challenging. This study presents a method for accurately monitoring the hexamethylenetetramine concentration in hexamethylenetetramine-acetic acid solution by integrating near-infrared spectroscopy with software techniques. Compared to the methods of zero mean standardization (Z-score), standard normal variate (SNV), first derivative (D1), second derivative (D2), and vector normalization (VN), the Savitzky-Golay first derivative (SGFD) method effectively addresses issues related to baseline drift, noise, and other interferences. This results in clearer identification of characteristic absorption peaks of different components and a significant improvement in the accuracy of the analysis. The study combined the uninformative variable elimination (UVE), differential evolution (DE), and moth flame optimization (MFO) algorithms to optimize a feature wavenumber selection method, which reduced the number of variables by 78.74 %, and the values of root mean square error of calibration set (RMSEC) and root mean square error of prediction (RMSEP) by 18.86 % and 30.38 %, respectively. Additionally, the analytical method was integrated with real-time monitoring software, enabling the monitoring of hexamethylenetetramine concentration. This integration supports the improvement of the quality and safety of the manufacturing process. The results demonstrate the potential application of near-infrared spectroscopy in real-time monitoring and automated control of chemical processes in the chemical industry.

Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine–acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques

Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA–acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction, first derivative, second derivative, and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination, competitive adaptive reweighted sampling, and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x–y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.

Reduction for asynchronous Boolean networks: elimination of negatively autoregulated components

To simplify the analysis of Boolean networks, a reduction in the number of components is often considered. A popular reduction method consists in eliminating components that are not autoregulated, using variable substitution. In this work, we show how this method can be extended, for asynchronous dynamics of Boolean networks, to the elimination of vertices that have a negative autoregulation, and study the effects on the dynamics and interaction structure. For elimination of non-autoregulated variables, the preservation of attractors is in general guaranteed only for fixed points. Here we give sufficient conditions for the preservation of complex attractors. The removal of so called mediator nodes (i.e. vertices with indegree and outdegree one) is often considered, and frequently does not affect the attractor landscape. We clarify that this is not always the case, and in some situations even subtle changes in the interaction structure can lead to a different asymptotic behaviour. Finally, we use properties of the more general elimination method introduced here to give an alternative proof for a bound on the number of attractors of asynchronous Boolean networks in terms of the cardinality of positive feedback vertex sets of the interaction graph.

Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol-Gasoline Blends by Raman Spectroscopy.

Methanol-gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol-gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol-gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)-PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol-gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.