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Pharmaceutical applications using NIR Technology in the cloud

机译：在云端使用NIR技术的药物应用

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摘要

NIR technology has been available for a long time, certainly more than 50 years. Without any doubt, it has found many niche applications, especially in the pharmaceutical, food, agriculture and other industries due to its flexibility. There are a number of advantages over other existing analytical technologies we can list, for example virtually no need for sample preparation; usually NIR does not demand sample destruction and subsequent discard; NIR provides fast results; NIR does not require extensive operator training and carries small operating costs. However, the key point about NIR technology is the fact that it's more related to statistics than chemistry or, in other words, we are more concerned about analyzing and distinguishing features within the data than looking deep into the chemical entities themselves. A simple scan reading in the NIR range usually involves huge inflows of data points. Usually we decompose the signals into hundreds of predictor variables and use complex algorithms to predict classes or quantify specific content. NIR is all about math, especially by converting chemical information into numbers. Easier said than done. A NIR signal is a very complex one. Usually the signal responses are not specific to a particular material, rather, each group's responses add up, thus providing low specificity of a spectral reading. This paper proposes a simple and efficient method to analyze and compare NIR spectra for the purpose of identifying the presence of active pharmaceutical ingredients in finished products using low cost NIR scanning devices connected to the internet cloud.
机译：NIR技术已经使用了很长时间，当然已经超过了50年。毫无疑问，由于其灵活性，它已经发现了许多利基应用，特别是在制药，食品，农业和其他行业。与我们列出的其他现有分析技术相比，它具有许多优点，例如，实际上不需要样品制备；通常，NIR不要求销毁样本并随后丢弃； NIR提供快速结果； NIR不需要大量的操作人员培训，并且运营成本低。但是，关于NIR技术的关键是事实，即它与统计数据比化学数据更相关，或者换句话说，与深入研究化学实体本身相比，我们更关注分析和区分数据中的特征。在NIR范围内进行简单的扫描读取通常会涉及大量的数据点流入。通常，我们将信号分解为数百个预测变量，并使用复杂的算法预测类或量化特定内容。 NIR与数学有关，特别是将化学信息转换为数字。说起来容易做起来难。 NIR信号非常复杂。通常，信号响应并不特定于特定材料，而是每个组的响应加起来，因此光谱读数的特异性较低。本文提出了一种简单有效的方法来分析和比较NIR光谱，以便使用连接到互联网云的低成本NIR扫描设备来识别成品中活性药物成分的存在。

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