On the assessment of citric acid in aqueous solution and fruit titratable acidity levels using SWNIRS : (a) citric acid solutions

Shortwave (700 to 1100 nm) near infrared spectroscopy using diode array instrumentation suited to in-line or field portable instrumentation was considered in context of the assessment of titratable acidity (TTA) in intact fruit. A searching window method was used to optimise the wavelength window for model development (around 750 – 950 nm). In pure citric acid – water mixtures (mean + SD of 5.05 + 6.65 g citric acid /100 ml), a RMSECV = 0.34 and R = 0.999 was achieved, while if the matrix was varied with 0, 10 and 13% sucrose, a RMSECV = 0.59 and a R = 0.996 was achieved. Models developed on spectra collected of the cut surface of a lime fruit (mean + SD of 7.3 + 0.51 g citric acid equivalents/100 ml), possessed a RMSECV = 0.17, R = 0.93, and a SDR = 3.0 (where SDR = SD/RMSECV), comparable with prediction results (RMSEP = 0.16, R = 0.89, bias = -0.03, and SDR = 2.4). For intact lime fruit, model calibration results (RMSECV = 0.16, R = 0.92, and SDR = 3.1) were markedly better than prediction results (RMSEP = 0.30, R = 0.70, bias = -0.07, SDR = 2.1). For a low TTA product, peach (with spectra collected across fruit maturity stages; mean + SD of 0.88 + 0.17), model calibration results were relatively poor (RMSECV = 0.09, R = 0.83, SDR = 1.8), while in prediction the model failed (RMSEP = 0.104, R = 0.05, bias = 0.02, SDR = 0.9). We conclude that SWNIR is not appropriate for assessment of the acidity of intact low TTA fruit, and has limited use for high TTA fruit. The method has value for rapid assessment of the TTA of juice extracted from moderate and high TTA fruit.

在评估完整果实可滴定酸度（TTA）的背景下，对使用二极管阵列仪器进行的短波近红外光谱（700至1100纳米）进行了考量，该仪器适用于在线或现场便携式仪器。为优化模型开发中的波长窗口（约750至950纳米），采用了搜索窗口法。在纯柠檬酸-水混合物（平均+标准差为5.05+6.65克柠檬酸/100毫升）中，实现了RMSECV = 0.34和R = 0.999，而当基质中添加0、10和13%的蔗糖时，则达到了RMSECV = 0.59和R = 0.996。在收集于青柠果实切面光谱（平均+标准差为7.3+0.51克柠檬酸当量/100毫升）的基础上开发的模型，其RMSECV = 0.17，R = 0.93，SDR = 3.0（其中SDR = SD/RMSECV），与预测结果（RMSEP = 0.16，R = 0.89，偏差 = -0.03，SDR = 2.4）相当。对于完整的青柠果实，模型校准结果（RMSECV = 0.16，R = 0.92，SDR = 3.1）显著优于预测结果（RMSEP = 0.30，R = 0.70，偏差 = -0.07，SDR = 2.1）。对于低TTA产品，如桃（在不同成熟阶段收集光谱；平均+标准差为0.88+0.17），模型校准结果相对较差（RMSECV = 0.09，R = 0.83，SDR = 1.8），而在预测中模型则失效（RMSEP = 0.104，R = 0.05，偏差 = 0.02，SDR = 0.9）。我们得出结论，短波近红外光谱（SWNIR）不适用于评估低TTA完整果实的酸度，且对于高TTA果实的使用也受到限制。该方法对于快速评估中等和高TTA水果果汁中的TTA具有价值。