Chemometrics and Intelligent Laboratory Systems最新文献_第2页

Chemometrics and electrochemistry joined hands to develop a novel and intelligent electronic device for simultaneous determination of malathion and diazinon in fruit juices: A progress in multidisciplinary studies 化学计量学和电化学联手开发了一种新型智能电子设备，用于同时测定果汁中的马拉硫磷和二嗪农：多学科研究的进展

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-18 DOI: 10.1016/j.chemolab.2024.105249

Leila Zare , Ehsan Sadeghi , Meghdad Pirsaheb , Maziar Farshadnia , Ali R. Jalalvand

In this work, chemometrics and electrochemistry connected to each other to open a new way for assisting food industry specialists based on developing a novel electrochemical sensor for simultaneous determination of malathion (MT) and diazinon (DZ) in the presence of patulin (PT) and citrinin (CT) as uncalibrated interference in fruit juices. The sensor was fabricated based on modification of a glassy carbon electrode (GCE) by chitosan-ionic liquid (Ch-IL), electrodeposition of gold nanoparticles (Au NPs), drop-casting of multiwalled carbon nanotubes-IL (MWCNTs-IL), and electrochemical synthesis of dual templates molecularly imprinted polymers (DTMIPs) in which MT and DZ were used as templates. Effects of experimental variables on structure and response of the sensor were screened and optimized by Min Run screening and central composite design, respectively. After optimization, the third-order hydrodynamic differential pulse voltammetric (HDPV) data were generated based on changing modulation times and modulation amplitudes as instrumental parameters and modeled by N-PLS/RTL, U-PLS/RTL, U-PCA/RTL, APARAFAC, PARAFAC2 and MCR-ALS to select the best one to assist the sensor for ultra selective simultaneous determination of MT and DZ in the presence of PT and CT as uncalibrated interference in fruit samples. The results confirmed the MCR-ALS was the best assistance for DTMIPs/MWCNTs-IL/Au NPs/Ch-IL/GCE for simultaneous determination of MT and DZ in the presence of PT and CT as uncalibrated interference in both synthetic and real samples. Performance of the sensor assisted by MCR-ALS for ultra selective simultaneous determination of MT (0.1 pM–12.5 pM, LOD = 0.01 pM) and DZ (0.25 pM–8.5 pM, LOD = 0.15 pM) was really admirable which was comparable with HPLC with UV detection while it was faster, simpler and low-cost in comparison to HPLC-UV which motivated us to introduce it as a reliable method to assist food industry specialists for quality assurance purposes.

在这项工作中，化学计量学和电化学相互结合，为食品工业专家开辟了一条新的途径，即开发一种新型电化学传感器，用于在果汁中同时测定马拉硫磷（MT）和二嗪农（DZ），以及作为非校准干扰的棒曲霉素（PT）和柠檬霉素（CT）。传感器的制作基于壳聚糖-离子液体（Ch-IL）对玻璃碳电极（GCE）的改性、金纳米粒子（Au NPs）的电沉积、多壁碳纳米管-IL（MWCNTs-IL）的滴铸以及以 MT 和 DZ 为模板的双模板分子印迹聚合物（DTMIPs）的电化学合成。实验变量对传感器结构和响应的影响分别通过 Min Run 筛选和中心复合设计进行了筛选和优化。优化后，以改变调制时间和调制幅度为仪器参数，生成三阶流体动力差分脉冲伏安法（HDPV）数据，并通过 N-PLS/RTL、U-PLS/RTL、U-PCA/RTL、APARAFAC、PARAFAC2 和 MCR-ALS 建模，选择最佳模型辅助传感器在水果样品中存在 PT 和 CT 未校准干扰的情况下超选择性地同时测定 MT 和 DZ。结果表明，MCR-ALS 是 DTMIPs/MWCNTs-IL/Au NPs/Ch-IL/GCE 在合成样品和实际样品中同时测定 MT 和 DZ（存在 PT 和 CT 未校准干扰）的最佳辅助传感器。在 MCR-ALS 的辅助下，传感器在同时测定 MT（0.1 pM-12.5 pM，LOD = 0.01 pM）和 DZ（0.25 pM-8.5 pM，LOD = 0.15 pM）的超选择性方面表现非常出色，可与带有紫外检测功能的 HPLC 相媲美，而与 HPLC-UV 相比，该方法更快、更简单、成本更低，这促使我们将其作为一种可靠的方法引入食品工业专家的质量保证工作中。

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引用次数: 0

Temperature correction of near-infrared spectra of raw milk 生乳近红外光谱的温度校正

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-18 DOI: 10.1016/j.chemolab.2024.105251

Jose A. Diaz-Olivares , Stef Grauwels , Xinyue Fu , Ines Adriaens , Wouter Saeys , Ryad Bendoula , Jean-Michel Roger , Ben Aernouts

Accurate milk composition analysis is crucial for improving product quality, economic efficiency, and animal health in the dairy industry. Near-infrared (NIR) spectroscopy can quantify milk composition quickly and nondestructively. However, external factors, such as temperature fluctuations, can alter the molecular vibrations and hydrogen bonding in milk, altering the NIR spectra and leading to errors in predicting key constituents such as fat, protein, and lactose. This study compares the effectiveness of Piecewise Direct Standardization (PDS), Continuous PDS (CPDS), External Parameter Orthogonalization (EPO), and Dynamic Orthogonal Projection (DOP in correcting the impact of temperature-induced variations on predictions in milk long-wave NIR spectra (LW-NIR, 1000–1700 nm).

A total of 270 raw milk samples were analyzed, collecting both reflectance and transmittance spectra at five different temperatures (20 °C, 25 °C, 30 °C, 35 °C, and 40 °C). The experimental setup ensured precise temperature control and accurate spectral measurements. PLSR models were calibrated at 30 °C to predict milk fat, protein, and lactose content. The performance of these models was assessed before and after applying the temperature correction methods, with a primary focus on reflectance spectra.

Results indicate that EPO and DOP significantly enhance model robustness and prediction accuracy across all temperatures, outperforming PDS and CPDS, especially for lactose prediction. These orthogonalization methods were compared against PLSR models calibrated with spectra from all temperatures. EPO and DOP showed comparable or superior performance, highlighting their effectiveness without requiring extensive temperature-specific calibration data. These findings suggest that orthogonalization methods are particularly suitable for in-line milk quality measurements under farm conditions where temperature control is challenging. This study highlights the potential of advanced chemometric techniques to improve real-time, on-farm milk composition analysis, facilitating better farm management and enhanced dairy product quality.

准确的牛奶成分分析对于提高乳制品行业的产品质量、经济效益和动物健康至关重要。近红外（NIR）光谱可快速、无损地量化牛奶成分。然而，温度波动等外部因素会改变牛奶中的分子振动和氢键，从而改变近红外光谱，导致对脂肪、蛋白质和乳糖等主要成分的预测出现误差。本研究比较了分片直接标准化（PDS）、连续 PDS（CPDS）、外部参数正交化（EPO）和动态正交投影（DOP）在校正温度引起的变化对牛奶长波近红外光谱（LW-NIR，1000-1700 nm）预测的影响方面的有效性。共分析了 270 个原奶样品，收集了五个不同温度（20 °C、25 °C、30 °C、35 °C和 40 °C）下的反射和透射光谱。实验装置确保了精确的温度控制和准确的光谱测量。在 30 °C 时校准 PLSR 模型，以预测牛奶中的脂肪、蛋白质和乳糖含量。结果表明，EPO 和 DOP 显著提高了模型在所有温度下的稳健性和预测准确性，其性能优于 PDS 和 CPDS，尤其是在乳糖预测方面。这些正交化方法与使用所有温度光谱校准的 PLSR 模型进行了比较。EPO 和 DOP 的性能相当或更优，这表明它们无需大量特定温度的校准数据即可发挥功效。这些研究结果表明，正交化方法特别适用于温度控制难度较大的牧场条件下的在线牛奶质量测量。这项研究凸显了先进的化学计量学技术在改善实时牧场牛奶成分分析方面的潜力，有助于改善牧场管理和提高乳制品质量。

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引用次数: 0

Mathematical modeling of ions adsorption from water/wastewater sources via porous materials: A machine learning-based approach 多孔材料对水/废水源中离子吸附的数学建模：基于机器学习的方法

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-16 DOI: 10.1016/j.chemolab.2024.105250

Guang Yang , Nadhir N.A. Jafar , Rafid Jihad Albadr , Mariem Alwan , Zainab Sadeq Yousif , Suhair Mohammad Husein Kamona , Safaa Mohammed Ibrahim , Usama S. Altimari , Ashwaq Talib Kareem , Raghu Jettie , Raaid Alubady , Ahmed Alawadi

This paper developed the predictive modeling of substance concentration (C) utilizing the input parameters x and y, for analysis of adsorption process. Employing three distinct machine learning models—Multilayer Perceptron (MLP), polynomial regression (PR), and Support Vector Machine (SVM)—the study investigates the efficacy of models in capturing the relationships between the inputs and output. The models are trained from data obtained from mass transfer calculations for removal of solute from solution via porous adsorbent. Furthermore, the hyper-parameters for each model are optimized through the utilization of the Political Optimizer (PO). The Multilayer Perceptron model emerges as a standout performer, showcasing an exceptional R-squared score of 0.9981, indicative of a robust fit to the data. Complemented by impressively low MAE and MSE values (7.94043E-01 and 2.0420E+00, respectively), the MLP model attests to its ability to provide accurate predictions and discern underlying patterns within the dataset. The polynomial regression model, while slightly trailing behind the MLP in terms of R-squared score (0.95929), revealed commendable predictive performance. Support Vector Machine also proves to be a formidable contender, boasting a robust R-squared score of 0.96055.

本文利用输入参数 x 和 y 建立了物质浓度（C）预测模型，用于分析吸附过程。该研究采用了三种不同的机器学习模型--多层感知器（MLP）、多项式回归（PR）和支持向量机（SVM）--研究模型在捕捉输入和输出之间关系方面的功效。这些模型是根据通过多孔吸附剂从溶液中去除溶质的传质计算所获得的数据进行训练的。此外，还利用政治优化器（PO）对每个模型的超参数进行了优化。多层感知器模型表现突出，R 方得分为 0.9981，显示出与数据的良好拟合。此外，MLP 模型的 MAE 值和 MSE 值（分别为 7.94043E-01 和 2.0420E+00）也很低，令人印象深刻，这证明它有能力提供准确的预测并识别数据集中的潜在模式。多项式回归模型虽然在 R 平方得分（0.95929）方面略逊于 MLP，但其预测性能值得称赞。事实证明，支持向量机也是一个强有力的竞争者，其 R 方得分为 0.96055。

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引用次数: 0

Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect 利用近红外-傅立叶变换红外光谱和化学计量学开发和验证用于麻风病早期诊断和治疗效果评估的新方法

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-16 DOI: 10.1016/j.chemolab.2024.105248

Andrea Cristina Novack , Alexandre de Fátima Cobre , Dile Pontarolo Stremel , Luana Mota Ferreira , Michel Leandro Campos , Roberto Pontarolo

Objective

Develop a new method for diagnosing leprosy and monitoring the pharmacological treatment effect of patients.

Material and methods

Plasma samples from patients diagnosed with leprosy (n = 211) who had not yet received any pharmacological treatment were collected at a basic health unit in Brazil. After treatment, samples were collected from the same patients (n = 125). Plasma samples from healthy volunteers were also collected (n = 179) and used as a control group. All samples were analyzed by Fourier transform mid-infrared spectrophotometry (MIR-FTIR). The spectral data of the samples were subjected to chemometric analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to predict diagnosis and monitor pharmacological treatment.

Results

The PCA model successfully distinguished among three sample classes: healthy individuals, pre-treatment leprosy patients, and post-treatment leprosy patients. The PLS-DA algorithm accurately classified healthy, treated, and diseased samples, facilitating both reliable diagnosis and treatment monitoring for leprosy. The model achieved a sensitivity of 97 %–100 %, specificity of 100 %, and accuracy ranging from 99 % to 100 %. Furthermore, when tested on plasma samples from patients with other conditions—renal failure (n = 1032), hypertriglyceridemia (n = 100), hypercholesterolemia (n = 100), and mixed dyslipidemia (n = 100)—the model correctly classified these as negative for leprosy, with diagnostic specificity between 93 % and 96 %.

Conclusion

The MIR-FTIR technique combined with PLS-DA analysis proved to be a highly effective tool for screening leprosy patients and monitoring treatment outcomes. Given its low cost, this method could be easily implemented in laboratories across emerging and low-income countries.

材料与方法在巴西的一家基层医疗机构采集了被诊断为麻风病但尚未接受任何药物治疗的患者（n = 211）的血浆样本。治疗后，又采集了相同患者（125 人）的血浆样本。同时还采集了健康志愿者的血浆样本（n = 179）作为对照组。所有样本均采用傅立叶变换中红外分光光度法（MIR-FTIR）进行分析。对样品的光谱数据进行了化学计量分析。主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）用于预测诊断和监测药物治疗。PLS-DA 算法准确地对健康样本、治疗样本和患病样本进行了分类，为麻风病的可靠诊断和治疗监测提供了便利。该模型的灵敏度为 97%-100%，特异度为 100%，准确度为 99%-100%。此外，在对患有肾功能衰竭（n = 1032）、高甘油三酯血症（n = 100）、高胆固醇血症（n = 100）和混合型血脂异常（n = 100）的患者的血浆样本进行测试时，该模型也能正确地将这些患者归类为麻风病阴性患者，诊断特异性在 93 % 到 96 % 之间。由于成本低廉，这种方法很容易在新兴国家和低收入国家的实验室中应用。

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引用次数: 0

LTFM: Long-tail few-shot module with loose coupling strategy for mineral spectral identification LTFM：采用松散耦合策略的矿物光谱识别长尾少拍模块

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-15 DOI: 10.1016/j.chemolab.2024.105247

Youpeng Fan , Yongchun Fang

In recent years, deep learning methods have exhibited superior performance in mineral identification when especially compared with conventional machine learning methods such as Support Vector Machine (SVM) and Partial Least Squares (PLS). Nevertheless, almost all of these deep learning methods pay more attention to improving and designing network structures, while neglecting the phenomenon of long-tail distribution in spectral data due to the inconsistency of ore distribution and the scarcity of several natural minerals. To alleviate the interference of majority categories on minority categories, we propose Long-Tail Few-shot Module (LTFM) which is inspired by rethinking the fashionable decoupling strategy that conducts primary representation learning and further classifier retrained on mineral spectral data. In particular, LTFM serves as a multi-expert mode, where these experts are respectively specialized in improving feature representation learning, mitigating the long-tail effect, and alleviating the interference of few shots. Additionally, the loose coupling learning strategy is introduced to facilitate primary representation learning and the subsequent additional experts to inherit this knowledge. Experiments on two publicly available spectral datasets show that the proposed LTFM significantly outperforms existing methods. In the end, extensive ablation studies are conducted to investigate the effectiveness, correctness, and robustness of our proposal.

近年来，与支持向量机（SVM）和偏最小二乘法（PLS）等传统机器学习方法相比，深度学习方法在矿物识别方面表现出更优越的性能。然而，由于矿石分布的不一致性和多种天然矿物的稀缺性，几乎所有这些深度学习方法都更注重网络结构的改进和设计，而忽视了光谱数据中的长尾分布现象。为了减轻多数类别对少数类别的干扰，我们提出了长尾少拍模块（LTFM），其灵感来自于对时下流行的解耦策略的反思，即在矿物光谱数据上进行初级表示学习和进一步的分类器再训练。具体而言，LTFM 是一种多专家模式，这些专家分别擅长改进特征表征学习、减轻长尾效应和缓解少镜头干扰。此外，LTFM 还引入了松耦合学习策略，以促进主要表征学习和后续附加专家对这些知识的继承。在两个公开的光谱数据集上进行的实验表明，所提出的 LTFM 明显优于现有方法。最后，我们还进行了广泛的消融研究，以调查我们建议的有效性、正确性和鲁棒性。

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引用次数: 0

Algae content prediction based on transfer learning and mean impact value 基于迁移学习和平均影响值的藻类含量预测

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-11 DOI: 10.1016/j.chemolab.2024.105244

Haonan Zhang, Xiaojing Ping, Haiying Wan, Xiaoli Luan, Fei Liu

To improve the prediction accuracies of algae contents in different water bodies, this paper proposes a chlorophyll-A prediction model method based on transfer learning(TL) and mean impact value(MIV) algorithm. Firstly, we preprocess the data collected from the Huai River, including removing the missing data and standardizing the preserved data. Then, the MIV algorithm is used to reduce the dimensionality of the data and determine the input variables of the model. Based on the selected input variables, the TL algorithm is introduced to establish the chlorophyll-A prediction model. The developed method can effectively enhance the prediction accuracy, especially when the number of samples is small. The simulation results verify the effectiveness and feasibility of the proposed prediction model.

为了提高不同水体藻类含量的预测精度，本文提出了一种基于迁移学习（TL）和平均影响值（MIV）算法的叶绿素-A预测模型方法。首先，我们对从淮河采集的数据进行预处理，包括去除缺失数据和标准化保留数据。然后，使用 MIV 算法降低数据维度，确定模型的输入变量。在选定输入变量的基础上，引入 TL 算法建立叶绿素-A 预测模型。所开发的方法能有效提高预测精度，尤其是在样本数量较少时。仿真结果验证了所建预测模型的有效性和可行性。

引用次数: 0

Recent applications of analytical quality-by-design methodology for chromatographic analysis: A review 色谱分析中分析质量控制方法的最新应用：综述

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-10 DOI: 10.1016/j.chemolab.2024.105243

Doan Thanh Xuan , Hue Minh Thi Nguyen , Vu Dang Hoang

Analytical Quality-by-Design (AQbD) represents a systematic methodology for method development. The pharmaceutical and biopharmaceutical industries have increasingly recognized and applied AQbD concepts, guided by the overall framework provided by ICH. AQbD is established to ensure that an analytical procedure is fit for its intended purpose throughout its entire lifecycle, leading to a well-understood and purpose-driven method. It guides each stage of the analytical process lifecycle by establishing the Analytical Target Profile (ATP), identifying critical method parameters (CMPs), and selecting critical method attributes (CMAs). By employing screening and response-surface experimental designs, significant factors are pinpointed and optimized through statistical analysis. This methodology aids in defining the design space or Method Operable Design Region (MODR) to ensure consistent method performance. This review delves into the foundational principles of AQbD for method development and presents its latest applications in the period 2019–2024 with reference to chromatographic analysis of both non-synthetic and synthetic compounds in different sample matrices. The implementation of AQbD proved to generate more robust chromatographic methods, enhancing their efficiency in the process. Nevertheless, its adoption can be hindered owing to the necessity for a comprehensive grasp of statistical analysis and experimental design, coupled with the absence of standardized directives or regulatory prerequisites.

通过设计提高分析质量（AQbD）是一种系统的方法论。在 ICH 提供的总体框架指导下，制药和生物制药行业越来越多地认可并应用 AQbD 概念。建立 AQbD 的目的是确保分析程序在其整个生命周期内都能满足其预期目的，从而形成一种理解透彻、目的明确的方法。它通过建立分析目标轮廓 (ATP)、确定关键方法参数 (CMP) 和选择关键方法属性 (CMA) 来指导分析过程生命周期的每个阶段。通过采用筛选和响应面实验设计，可以精确定位重要因素，并通过统计分析进行优化。这种方法有助于确定设计空间或方法可操作设计区域 (MODR)，以确保方法性能的一致性。本综述深入探讨了 AQbD 用于方法开发的基本原理，并介绍了其在 2019-2024 年期间的最新应用，涉及不同样品基质中非合成和合成化合物的色谱分析。事实证明，AQbD 的实施可生成更稳健的色谱方法，提高色谱过程的效率。然而，由于必须全面掌握统计分析和实验设计，再加上缺乏标准化指令或监管先决条件，该方法的采用可能会受到阻碍。

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引用次数: 0

Layer-wise-residual-driven approach for soft sensing in composite dynamic system based on slow and fast time-varying latent variables 基于慢速和快速时变潜变量的复合动态系统软传感分层-残差驱动方法

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-09 DOI: 10.1016/j.chemolab.2024.105245

Zhengxuan Zhang , Xu Yang , Jian Huang , Yuri A.W. Shardt

Driven by the requirements for a comprehensive understanding of composite dynamic systems in industrial processes, this paper investigates a new soft sensor for quality prediction based on slow and fast time-varying latent variables extraction using layer-wise residuals. First, the slow feature partial least squares were expanded into long-term dependency by introducing explicit expressions of the potential state of the process into the objective function. Then, the multilayer regression model for exploring composite dynamics driven by layer-wise residuals is developed using a serial structure that can extract both slow and fast time-varying latent variables that are completely orthogonal. Finally, the exponential-weighted partial least squares are proposed for extracting fast time-varying dynamic latent variables by learning the exponential decay properties of the time-series data correlation. Case studies on the industrial debutanizer and sulfur recovery unit show that the prediction accuracy of the proposed approach outperforms traditional methods.

为了全面了解工业流程中的复合动态系统，本文研究了一种基于慢速和快速时变潜变量提取的新型质量预测软传感器。首先，通过在目标函数中引入过程潜在状态的明确表达式，将慢速特征偏最小二乘法扩展为长期依赖性。然后，利用序列结构开发了用于探索由层向残差驱动的复合动力学的多层回归模型，该模型可以提取完全正交的慢速和快速时变潜变量。最后，通过学习时间序列数据相关性的指数衰减特性，提出了提取快速时变动态潜变量的指数加权偏最小二乘法。对工业去烷器和硫磺回收装置的案例研究表明，所提方法的预测精度优于传统方法。

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引用次数: 0

Applicability domain of a calibration model based on neural networks and infrared spectroscopy 基于神经网络和红外光谱的校准模型的适用范围

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-05 DOI: 10.1016/j.chemolab.2024.105242

M. Suliany Rodríguez-Barrios , Joan Ferré , M. Soledad Larrechi , Enric Ruiz

Artificial neural networks are used as calibration models in routine analytical determinations that involve spectroscopic data. To ensure that the model will generate reliable predictions for new samples, the applicability domain must be well defined. This article describes a strategy for establishing the limits of the applicability domain when the calibration model is a feed-forward neural network. The applicability domain was defined by two limits: 1) the 0.99 quantile of the squared Mahalanobis distance calculated from the network activations of the training set and 2) the 0.99 quantile of the reconstruction error of the training spectra using either an autoencoder network or a decoder network. A new sample with a squared Mahalanobis distance and/or spectral residuals beyond these limits is said to be outside the applicability domain, and the prediction is questionable. The approach was illustrated by predicting the density of diesel fuel samples from mid-infrared spectra and the fat content in meat from near-infrared spectra. The methodology could correctly detect anomalous spectra in prediction using either the autoencoder or the decoder.

在涉及光谱数据的常规分析测定中，人工神经网络被用作校准模型。为确保模型能对新样品生成可靠的预测，必须对适用域进行明确定义。本文介绍了当校准模型为前馈神经网络时确定适用域限制的策略。适用域由两个极限定义：1）根据训练集的网络激活计算出的马哈拉诺比距离平方的 0.99 量级；2）使用自动编码器网络或解码器网络对训练频谱重建误差的 0.99 量级。如果新样本的马哈拉诺比斯距离平方和/或频谱残差超出了这些限制，则称其超出了适用范围，预测结果值得怀疑。通过中红外光谱预测柴油样本的密度，以及通过近红外光谱预测肉类的脂肪含量，对该方法进行了说明。在使用自动编码器或解码器进行预测时，该方法都能正确检测到异常光谱。

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引用次数: 0

Machine learning based modeling for estimation of drug solubility in supercritical fluid by adjusting important parameters 基于机器学习的模型，通过调整重要参数估算药物在超临界流体中的溶解度

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2024-10-03 DOI: 10.1016/j.chemolab.2024.105241

Yaoyang Liu , Morug Salih Mahdi , Usama Kadem Radi , Ali Jihad , Ali Hamid AbdulHussein , Irshad Ahmad , Nasrin Mansuri , Mostafa Adnan Abdalrahman , Ahmed Alkhayyat , Ahmed Faisal

Here, we employed machine learning models to predict how well Capecitabine drug would dissolve in supercritical carbon dioxide as the green solvent. The vision is to investigate the drug suitability for processing of nanodrugs with enhanced bioavailability in the body. In the employed data set, P (pressure) and T (temperature) serve as inputs, and Y, the solubility, is the only output for building the models. This study uses DT (Decision Tree) and MLP (Multilayer perceptron) as the core models. However, the raw and individual form of conventional algorithms may not provide accurate and general results. Ensemble methods like boosting improve the model performance. Also, single and ensemble models mounted on these models have hyper-parameters whose optimization affects the final models. Meta-heuristic algorithms are popular for tuning hyper-parameters. In this research, we used a new hybrid framework by coupling the basic models with the Adaboost algorithm (as an ensemble method) and PO and CS algorithms (as optimizers) to obtain four different models. The MLP model boosted with Adaboost and tuned with PO algorithm showed the best fitting accuracy among all models. This model reduces the RMSE error rate to 1.71, MSE to 2.92, and MAE to 1.42.

在这里，我们采用机器学习模型来预测卡培他滨药物在作为绿色溶剂的超临界二氧化碳中的溶解度。我们的愿景是研究药物在体内生物利用度提高的纳米药物加工中的适用性。在采用的数据集中，P（压力）和 T（温度）是输入，Y（溶解度）是建立模型的唯一输出。本研究使用 DT（决策树）和 MLP（多层感知器）作为核心模型。然而，传统算法的原始和单独形式可能无法提供准确和通用的结果。增强等集合方法可以提高模型性能。此外，安装在这些模型上的单一模型和集合模型都有超参数，其优化会影响最终模型。元启发式算法是调整超参数的常用方法。在这项研究中，我们使用了一种新的混合框架，将基本模型与 Adaboost 算法（作为一种集合方法）以及 PO 和 CS 算法（作为优化器）结合起来，得到了四种不同的模型。在所有模型中，用 Adaboost 算法提升并用 PO 算法调整的 MLP 模型的拟合精度最高。该模型将 RMSE 误差率降至 1.71，MSE 降至 2.92，MAE 降至 1.42。

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引用次数: 0