Fast and Efficient NIRS Data Preprocessing and Modeling with Pinard

NIRS (Near-Infrared Spectroscopy) is a powerful analytical technique widely used in various fields such as pharmaceuticals, agriculture, and food industries. NIRS measures the light reflected from a sample after irradiating it with different wavelengths, providing valuable insights into the physical and chemical characteristics of the sample.

However, working with NIRS data can be challenging due to the large amount of information generated and the need for extensive preprocessing and modeling. This is where Pinard comes in. Pinard is a python package specifically developed for the preprocessing and processing of NIRS data. It extends scikit-learn pipelines, making it fast and efficient to develop prediction models for desired traits.

Example Implementations

Here are three example code implementations that demonstrate the capabilities of Pinard:

1. Data Loading and Splitting

x, y = utils.load_csv(xcal_csv, ycal_csv, x_hdr=0, y_hdr=0, remove_na=True)
train_index, test_index = train_test_split_idx(x, y=y, method="kennard_stone", metric="correlation" test_size=0.25, random_state=rd_seed)
X_train, y_train, X_test, y_test = x[train_index], y[train_index], x[test_index], y[test_index]

This code snippet loads NIRS data from CSV files and splits it into training and test sets using the Kennard Stone method based on the correlation metric. The resulting sets are then assigned to variables for further processing.

2. Preprocessing Pipeline

preprocessing = [
('id', pp.IdentityTransformer()),
('savgol', pp.SavitzkyGolay()),
('derivate', pp.Derivate()),
Pipeline([('_sg1',pp.SavitzkyGolay()),('_sg2',pp.SavitzkyGolay())])
]

pipeline = Pipeline([
('scaler', MinMaxScaler()),
('preprocessing', FeatureUnion(preprocessing)),
('PLS', sklearn.PLS())
])

This code snippet demonstrates the creation of a preprocessing pipeline using Pinard. The pipeline includes various preprocessing steps such as smoothing, derivative calculation, and nested pipelines. The data is scaled using the MinMaxScaler and then processed through the defined preprocessing steps before being fed into a partial least squares (PLS) model.

3. Model Training and Prediction

estimator = TransformedTargetRegressor(regressor=pipeline, transformer=MinMaxScaler())
estimator.fit(X_train, y_train)
Y_preds = estimator.predict(X_test)

This code snippet showcases the training and prediction process using Pinard. The TransformedTargetRegressor is used to apply the specified transformer (MinMaxScaler) to the target variable (y) before passing it to the regressor (pipeline). The estimator is then fitted to the training data, and predictions are made on the test data, resulting in the Y_preds variable.

Technology and Categorization

The technologies and packages utilized in these implementations are:

• Python
• Pinard
• scikit-learn
• MinMaxScaler
• FeatureUnion
• partial least squares (PLS)

Pinard falls under the categories of Data Analysis and Machine Learning. It simplifies the preprocessing and modeling of NIRS data, allowing for fast development and optimization of prediction models for chemical traits.

In conclusion, Pinard is a valuable tool for anyone working with NIRS data. Its extensive preprocessing capabilities, integration with scikit-learn, and efficient pipeline design make it easy to preprocess and model NIRS data for chemical analysis. Give it a try and take your NIRS analysis to the next level!

Note: This article is based on the Pinard documentation and examples available at https://github.com/GBeurier/pinard. Make sure to refer to the documentation and examples for more detailed usage and implementation guidance.